Part of Best Cheap LLM for High Volume

Best Value LLM

Qwen3.8 27B ranks first for Best Value LLM as of October 20, 2026.[1][2][3][4][5][6][7] It ranks #15 on LiveBench Instruction Following (72.66%) and #64 on LMArena (Elo 1438), fitting on a single H200 GPU at full 262,144 context length with 122.1 tok/s for one caller. Gemini 3.8 Flash scores within two points, so read the top two as close rather than as a measured gap. Watch out: Watch for understated vision token metrics where image tokens are excluded from usage.prompt_tokens, breaking cost comparisons. Gemini 3.8 Flash is the next-ranked alternative. It ranks #1 on LiveBench Instruction Following (81.41%) and #8 on LMArena (Elo 1496), with promotional pricing at $0.75 per 1M input tokens and $3.75 per 1M output tokens through 2026.

About this recommendation

Updated
Evidence through
Sources
20
Revision
v113

Decision audit

Why this result

Inspect the inputs and the computed order behind the recommendation.

Models screened

24

live candidates

Evaluation feeds

4

task-weighted

Winner coverage

72%

intended feed weight

Largest provider share

3 of 8

Google

Established source breadth. 16 citation families and 3 practitioner families provide attributed capability reports; 1 cautionary thread retained. The largest citation family contributes 27%.

The leading model changed. Qwen: Qwen3.8 27B leads the measured task comparison with 2 independent evaluation families. Community volume, release age and usage popularity add no ranking points. This category also weights its stated price or context criteria. The leading scores are close under this policy.

Sources evaluated

These are the weights used in scoring. Missing measurements are omitted from each model’s average. A feed with no result for any model released in the last 60 days is stale and does not score until it catches up.

winner: Qwen3.8 27B
Evaluation feedWeightWinner resultField measured
price weight
46%
83/10024/24
Route reliabilitydisabled; last successful check unknownunavailable
0%
feed unavailable0/24
LiveBench Instruction Following (2026-06-25)Observed 2026-10-18 · evaluation date not supplied · qwen3.8-27bfailing; last successful check 2026-10-18
31%
#1524/24
LMArena TextObserved 2026-10-15 · evaluation date not supplied · qwen3.8-27b
23%
#6424/24

Provider concentration

Each exact model is scored separately; provider identity is not a ranking input.

Google38%
  • Google3 models
  • deepseek1 model
  • Meta1 model
  • Moonshot AI1 model
  • Qwen1 model
  • Z.ai1 model

Decision table

Every published model is shown in computed order. Practitioner sources are distinct community threads, not the citations repeated in the prose below.

  1. 01Qwen3.8 27B78

    Qwen · coverage 72%

    #15 LiveBench Instruction Following · #64 LMArena Text

  2. 02Gemini 3.8 Flash77

    Google · coverage 72% · close score

    #1 LiveBench Instruction Following · #8 LMArena Text

  3. 03DeepSeek V4.1 Flash76

    deepseek · coverage 72%

    #25 LiveBench Instruction Following · #26 LMArena Text

  4. 04Gemini 3.7 Flash76

    Google · coverage 72%

    #2 LiveBench Instruction Following · #13 LMArena Text

  5. 05GLM 5.374

    Z.ai · coverage 72%

    #21 LMArena Text · #27 LiveBench Instruction Following

  6. 06Kimi K372

    Moonshot AI · coverage 72%

    #14 LMArena Text · #20 LiveBench Instruction Following

  7. 07Muse Spark 1.371

    Meta · coverage 72%

    #4 LiveBench Instruction Following · #9 LMArena Text

  8. 08Gemini 3.6 Flash70

    Google · coverage 72%

    #8 LiveBench Instruction Following · #18 LMArena Text

Task score averages the available, task-weighted measurements. Benchmark positions compare models matched to our catalog. Models measured by at least two independent evaluator families are considered first. Missing tests, practitioner report counts, popularity and release age add no points. Coverage describes evidence breadth, not a probability of correctness. Scores within two points are shown as close under this policy; this is not a statistical confidence interval. When the leading scores are that close, the model measured by more evaluator families ranks first.

  1. 01

    Qwen3.8 27BQwen · $0.040 in / $1.35 out per M tokens · 1M context · open-weight

    Qwen3.8 27B ranks #15 on LiveBench Instruction Following (72.66%) and #64 on LMArena (Elo 1438), fitting on a single H200 GPU at full 262,144 context length with 122.1 tok/s for one caller.

    Best when: Self-host on H200 for full 262K context without multi-node overhead, achieving 122.1 tok/s single-caller throughput and 3957.3 tok/s at 1024 concurrency.
    Source 3
    “Qwen3.8-27B is a 27B dense hybrid-attention model with a vision tower and an MTP draft head. It fits one H200 GPU at the full 262144 context, so it queues faster than any recipe that needs a whole node. | | Measured | | --- | --- | | One caller | 122.1 tok/s | | Concurrency 1024 | 3957.3 tok/s, still rising | | Context | 262144, the checkpoint maximum | | KV cache | 1,804,253 tokens, 6.88 full-length requests at once | | Hardware | 1 H200 GPU, TP1, no NCCL | Scope - New recipe at recipes/Qwen3.…”
    mmshadOpen original ↗

    More tips

    • Deploy on RTX 3090 with 18GB weights where ~7GB remaining VRAM supports practical local inference.
      Source 8
      “Can't help much on the auto-approve side, but on a single 3090 the timeouts and empty replies are often a memory/context issue on the Ollama side, so here's the budget for qwen3.8:27b (estimates, not measured on your box): - Weights: the qwen3.8:27b tag is 18 GB (~16.8 GiB, vision tower included). That leaves ~7 GiB of the 24, minus ~1-1.5 GiB for the CUDA context and compute buffers. - KV cache: Qwen3.8 27B only keeps a KV cache in 16 of its 64 layers (the other 48 are linear attention wit…”
      159753a52Open original ↗

    Watch out for

    • Watch for understated vision token metrics where image tokens are excluded from usage.prompt_tokens, breaking cost comparisons.
      Source 4
      “Related: #313. Problem For an OCR call on a full-size photo through LM Studio (qwen/qwen3.8-27b, image_url with detail: "high"), the response usage.prompt_tokens is ~230–260, while the server log for the same request shows uncached_tokens=3334. The sample's meta.promptTokens (and so totalTokens and costUsd) therefore excludes the image tokens. Token metrics for vision calls are understated, which breaks token comparisons between models in experiments (#313). Evidence - LM…”
      abi83Open original ↗
    • Avoid with prefix caching and speculative decoding together, as EAGLE/MTP causes 30-40% batch throughput loss on prefix-reusing workloads.
      Source 9
      “EAGLE/MTP prefix-cache last-block drop causes a 1,648-token recompute per hit on one hybrid Qwen3.8 GDN layout — ~30-40% batch throughput loss on prefix-reusing workloads with speculative decoding enabled Environment - vLLM 0.26.1rc1.dev926+gb05ae5dc0, V1/V2 model runner, async scheduling - Dual RTX 5090 TP=2, FP8 weights + FP8 KV, prefix caching on - Model: Qwen3.8-27B (hybrid: GDN/Mamba layers + full attention → 15 MambaSpec KV groups + full-attention group; scheduler/hash block = 1648 t…”
      Suppressor72Open original ↗
  2. 02

    Gemini 3.8 FlashGoogle · $0.83 in / $4.13 out per M tokens · 1.0M context

    Gemini 3.8 Flash ranks #1 on LiveBench Instruction Following (81.41%) and #8 on LMArena (Elo 1496), with promotional pricing at $0.75 per 1M input tokens and $3.75 per 1M output tokens through 2026.

    Best when: Use for instruction-following tasks where benchmark-topping accuracy matters, as it scores #1 of 59 on LiveBench Instruction Following at 81.41%.
    Source 5
    “Within the 59 catalog models matched to this evaluation: Scores 81.41% on LiveBench Instruction Following (#1 of 59), including paraphrasing, simplifying, story generation, and summarization.”
    LiveBench Instruction FollowingOpen original ↗

    More tips

    • Deploy for cost-sensitive batch workflows where confirmed results at $0.303 each outperform Claude Opus 5 at $0.609 and Claude Sonnet 5 at $0.726.
      Source 10
      “The runs Four models measured under the current protocol, in one batch: | Model | Total | Cost | $/confirmed | Confirmed / reported | | --- | --- | --- | --- | --- | | google/gemini-3.8-flash | 20m12s | $3.33 | $0.303 | 11 / 11 | | anthropic/claude-opus-5 | 27m36s | $10.35 | $0.609 | 17 / 17 | | anthropic/claude-sonnet-5 | 39m51s | $5.81 | $0.726 | 8 / 8 | | anthropic/claude-haiku-4.5 | 10m03s | $0.64 | $0.320 | 2 / 7 | Each cost reconstructed exactly at OpenRouter's published pr…”
      using-systemOpen original ↗
    • Route through Vertex for half the per-token price of Gemini 3.6 Flash with roughly twice the speed and no token cap overruns in testing.
      Source 11
      “Summary Switch the Cloudflare Worker's LLM from google/gemini-3.6-flash to vertex/gemini-3.8-flash on Requesty. Same output quality on the live batch prompt, half the per-token price, roughly twice as fast, and it never overran the token cap in testing (3.6 Flash did once in four refills, forcing a full strict-prompt retry). Changes (all in worker/) 1. wrangler.toml: set UR_MODEL = "vertex/gemini-3.8-flash". 2. wrangler.toml: bump UR_GENERATION_VERSION from 2 to 3 so dev…”
      alexsiri7Open original ↗
  3. 03

    DeepSeek V4.1 Flashdeepseek · $0.056 in / $0.12 out per M tokens · 1.0M context · open-weight

    DeepSeek V4.1 Flash ranks #26 on LMArena (Elo 1475) and #25 on LiveBench Instruction Following (70.03%), with worker-tier evaluation showing $4.32 per solved task versus $9.05 on Opus high.

    Best when: Deploy for cost-per-solve optimization where $4.32 per task undercuts Opus high by more than half.
    Source 12
    “Completed and merged into main in pull request https://github.com/Wladefant/super-board/pull/342 (commit 3c360188711ef811452a2643c9c4265c4ce5db5f). Authoritative deliverable: https://github.com/Wladefant/super-board/blob/3c360188711ef811452a2643c9c4265c4ce5db5f/docs/research/agent-benchmarks-2026-09.md Comprehensive benchmark research delivered: Artificial Analysis Terminal-Bench 4.0 analysis, DeepSeek V4.1 Flash worker tier evaluation ($4.32/solved vs $9.05 on Opus high), cost-per-solve me…”
    WladefantOpen original ↗

    Watch out for

    • Watch for reliability issues on SM120 TP4 with engram offloading that caused reversion to GLM53 flash in production.
      Source 13
      “Same here, and I kind of had this with all ds 4 flash models as well... also running the stock snapshot on SM120 TP4 with engram offloading. This really makes it unreliable to use for us, and we reverted to GLM53 flash now, although I really like the much faster architecture of ds 4/4.1 flash!”
      opt-devOpen original ↗
  4. 04

    Gemini 3.7 FlashGoogle · $0.75 in / $3.75 out per M tokens · 1.0M context

    Gemini 3.7 Flash ranks #2 on LiveBench Instruction Following (79.93%) and #13 on LMArena (Elo 1488), sharing the same promotional pricing structure as 3.8 Flash.

    Best when: Select for high-accuracy instruction following at #2 rank on LiveBench with promotional pricing identical to 3.8 Flash.
    Source 14
    “Within the 59 catalog models matched to this evaluation: Scores 79.93% on LiveBench Instruction Following (#2 of 59), including paraphrasing, simplifying, story generation, and summarization.”
    LiveBench Instruction FollowingOpen original ↗
    Source 7
    “Detected on 2026-10-03. Models removed - Copilot — Claude Opus 4.7 removed - Copilot — Gemini 3.5 Flash removed - Copilot — Gemini 3.6 Flash removed - Copilot — Kimi K2.7 Code removed - Copilot — Claude Opus 4.7 removed - Copilot — Gemini 3.5 Flash removed Changed notes - Copilot — Gemini 3.7 Flash and Gemini 3.8 Flash are available at the promotional pricing of $0.75 per 1M input tokens, $0.075 per 1M cached input tokens, and $3.75 per 1M output tokens throu…”
    github-actions[bot]Open original ↗

    More tips

    • Use as a lower-latency alternative when 4.2s average response time is acceptable for the task.
      Source 15
      “追加評価: gpt-5.6-luna（2026-08-17） | | gpt-4o-mini | gemini-3.7-flash | gpt-5.6-luna | |---|---|---|---| | 精度 | 5/8 | 7/8 | 7/8 | | 平均レイテンシ | 3.1s | 4.2s | 4.3s | | 単価（in/out per 1M） | $0.15/$0.60（legacy） | $0.75/$3.75 → 2027年に$1.50/$7.50へ値上げ | $0.20/$1.20（値上げ予定なし） | | draft 1回概算 | 〜$0.0007 | 〜$0.004 | 〜$0.0011 | - Luna は gemini-3.7-flash と同じ 7/8（落ちたのは全モデル共通の conflict warnings ケースのみ）。曖昧期限・前日準備も正しく分解 - priority 判定は gemini がやや上（Luna は起床/出社/帰宅に mandatory を付ける過剰傾向。ただし商談=external/…”
      devacxx01Open original ↗

    Watch out for

    • Expect price increases to $1.50/$7.50 per 1M tokens after January 1, 2027, reducing long-term value.
      Source 15
      “追加評価: gpt-5.6-luna（2026-08-17） | | gpt-4o-mini | gemini-3.7-flash | gpt-5.6-luna | |---|---|---|---| | 精度 | 5/8 | 7/8 | 7/8 | | 平均レイテンシ | 3.1s | 4.2s | 4.3s | | 単価（in/out per 1M） | $0.15/$0.60（legacy） | $0.75/$3.75 → 2027年に$1.50/$7.50へ値上げ | $0.20/$1.20（値上げ予定なし） | | draft 1回概算 | 〜$0.0007 | 〜$0.004 | 〜$0.0011 | - Luna は gemini-3.7-flash と同じ 7/8（落ちたのは全モデル共通の conflict warnings ケースのみ）。曖昧期限・前日準備も正しく分解 - priority 判定は gemini がやや上（Luna は起床/出社/帰宅に mandatory を付ける過剰傾向。ただし商談=external/…”
      devacxx01Open original ↗
  5. 05

    GLM 5.3Z.ai · $0.079 in / $1.40 out per M tokens · 1.0M context · open-weight

    GLM 5.3 ranks #21 on LMArena (Elo 1479) and #27 on LiveBench Instruction Following (69.3%), with monthly costs ranging from $106 to $342 across providers for 100M input + 20M output tokens.

    Best when: Shop providers aggressively, as the same usage costs $106 at DeepInfra versus $342 at the most expensive option.
    Source 16
    “Prices per provider from OpenRouter's list, 23 Sep. Monthly cost assumes 100M input + 20M output tokens. | Provider | $/M input | $/M output | Monthly | |---|---|---|---| | DeepInfra | 0.56 | 2.50 | $106 | | Novita | 0.78 | 2.46 | $127 | | Reka | 0.76 | 2.57 | $127 | | Io Net | 0.81 | 2.73 | $135 | | Phala | 0.84 | 2.64 | $137 | Same model, same usage: $106 at the cheapest provider, $342 at the most expensive. Live prices, precision and uptime for all 31, with a calculator for your own usag…”
    yash-711Open original ↗

    Watch out for

    • Watch for missing published pricing on API endpoints despite provider announcements of availability.
      Source 17
      “Observed Z.AI announced GLM-5.3 to all API users on 2026-08-22: same base model as GLM-5.2, same wire shape, "one parameter change" to adopt, and — per the launch note — "available at the same price". #6988 catalogued the route the same day but deliberately left it unpriced ("Z.AI has not added it to the public per-token rate card"). Two problems follow from the state that landed: 1. zai/glm-5.3 carries no pricing. docs.z.ai's rate card now lists GLM-5.3 at $1.40 in / $4.40 out / $…”
      kennethsinderOpen original ↗
  6. 06

    Kimi K3Moonshot AI · $0.24 in / $11.94 out per M tokens · 1.0M context · open-weight

    Kimi K3 ranks #14 of 152 on LMArena's overall text arena (Elo 1487), based on blind human preference votes.

    Best when: Its strongest result for this task is on LMArena Text, which measures general reasoning and instruction following.
    Source 18
    “Within the 152 catalog models matched to this evaluation: Ranks #14 of 152 on LMArena's overall text arena (Elo 1487), based on blind human preference votes.”
    LMArena text arenaOpen original ↗
  7. 07

    Muse Spark 1.3Meta · $1.25 in / $4.25 out per M tokens · 1.0M context

    Muse Spark 1.3 ranks #4 on LiveBench Instruction Following (78%) and #9 on LMArena (Elo 1494), with strong human preference scores indicating good practical utility.

    Best when: Use in agent reviewer roles where $0.0861 for 147.1K tokens provides cost-effective validation.
    Source 19
    “Corrección: los costos reales, medidos en DevPass Primera facturación real de las lanes de este issue. Mis proyecciones estaban 6-10× por encima. | Rol | Modelo real | Req | Tokens | Costo | | --- | --- | ---: | ---: | ---: | | explorer | azure/gpt-5.6-luna | 5 | 127.2K | $0.0104 | | reviewer | meta/muse-spark-1.3 | 5 | 147.1K | $0.0861 | | (auxiliar) | google-vertex/gemini-3.7-flash | 2 | 3.0K | $0.0023 | | | | 12 | 277.3K | $0.0988 | Las dos lanes completas de…”
    ingamartinezOpen original ↗

    No practitioner reports are linked yet, so no cautions are listed. Absence of reports is not evidence of reliability.

  8. 08

    Gemini 3.6 FlashGoogle · $0.75 in / $3.75 out per M tokens · 1.0M context

    Gemini 3.6 Flash ranks #8 on LiveBench Instruction Following (75.37%) and #18 on LMArena (Elo 1483), with curated pricing at $1.50/$7.50 and thinking ladder support.

    Best when: Deploy for ask/propose actions in cost-optimized routing where verified reachability and HTTP 200 responses confirm reliable access.
    Source 20
    “Context Unified spec item 3b — per-action Gemini model routing (economics doc E-2: ~32% blended cost cut, "one URL constant plus a per-action switch, no product change"). Routing: ask/propose → gemini-3.6-flash, hint → gemini-3.5-flash-lite. Both targets are VERIFIED REACHABLE on the prod key — see the 2026-07-28 curl record in #838's comments (AIE-04: all three candidates HTTP 200; AIE-05: thinkingBudget: 0 verified honored with a control). Note the route's line-69 comme…”
    thomgabrielOpen original ↗

Frequently asked

What is the best model for Best Value LLM?
Qwen3.8 27B ranks first as of October 20, 2026. Qwen3.8 27B ranks #15 on LiveBench Instruction Following (72.66%) and #64 on LMArena (Elo 1438), fitting on a single H200 GPU at full 262,144 context length with 122.1 tok/s for one caller.[1][2][3]
What should I watch out for with Qwen3.8 27B?
Watch for understated vision token metrics where image tokens are excluded from usage.prompt_tokens, breaking cost comparisons.[4]
What is the best alternative to Qwen3.8 27B?
Gemini 3.8 Flash ranks second. Gemini 3.8 Flash ranks #1 on LiveBench Instruction Following (81.41%) and #8 on LMArena (Elo 1496), with promotional pricing at $0.75 per 1M input tokens and $3.75 per 1M output tokens through 2026.[5][6][7]
Can I self-host the top pick for Best Value LLM?
Yes. Qwen3.8 27B is open-weight, so it can run on your own hardware as well as through hosted routes.[1][2][3]

Sources

  1. 1

    “Within the 59 catalog models matched to this evaluation: Scores 72.66% on LiveBench Instruction Following (#15 of 59), including paraphrasing, simplifying, story generation, and summarization.”

    LiveBench Instruction Following · Benchmark · Oct 18, 2026
  2. 2

    “Within the 152 catalog models matched to this evaluation: Ranks #64 of 152 on LMArena's overall text arena (Elo 1438), based on blind human preference votes.”

    LMArena text arena · Benchmark · Oct 15, 2026
  3. 3

    “Qwen3.8-27B is a 27B dense hybrid-attention model with a vision tower and an MTP draft head. It fits one H200 GPU at the full 262144 context, so it queues faster than any recipe that needs a whole node. | | Measured | | --- | --- | | One caller | 122.1 tok/s | | Concurrency 1024 | 3957.3 tok/s, still rising | | Context | 262144, the checkpoint maximum | | KV cache | 1,804,253 tokens, 6.88 full-length requests at once | | Hardware | 1 H200 GPU, TP1, no NCCL | Scope - New recipe at recipes/Qwen3.…”

    mmshad · GitHub · Aug 26, 2026
  4. 4

    “Related: #313. Problem For an OCR call on a full-size photo through LM Studio (qwen/qwen3.8-27b, image_url with detail: "high"), the response usage.prompt_tokens is ~230–260, while the server log for the same request shows uncached_tokens=3334. The sample's meta.promptTokens (and so totalTokens and costUsd) therefore excludes the image tokens. Token metrics for vision calls are understated, which breaks token comparisons between models in experiments (#313). Evidence - LM…”

    abi83 · GitHub · Oct 13, 2026
  5. 5

    “Within the 59 catalog models matched to this evaluation: Scores 81.41% on LiveBench Instruction Following (#1 of 59), including paraphrasing, simplifying, story generation, and summarization.”

    LiveBench Instruction Following · Benchmark · Oct 18, 2026
  6. 6

    “Within the 152 catalog models matched to this evaluation: Ranks #8 of 152 on LMArena's overall text arena (Elo 1496), based on blind human preference votes.”

    LMArena text arena · Benchmark · Oct 15, 2026
  7. 7

    “Detected on 2026-10-03. Models removed - Copilot — Claude Opus 4.7 removed - Copilot — Gemini 3.5 Flash removed - Copilot — Gemini 3.6 Flash removed - Copilot — Kimi K2.7 Code removed - Copilot — Claude Opus 4.7 removed - Copilot — Gemini 3.5 Flash removed Changed notes - Copilot — Gemini 3.7 Flash and Gemini 3.8 Flash are available at the promotional pricing of $0.75 per 1M input tokens, $0.075 per 1M cached input tokens, and $3.75 per 1M output tokens throu…”

    github-actions[bot] · GitHub · Oct 3, 2026
  8. 8

    “Can't help much on the auto-approve side, but on a single 3090 the timeouts and empty replies are often a memory/context issue on the Ollama side, so here's the budget for qwen3.8:27b (estimates, not measured on your box): - Weights: the qwen3.8:27b tag is 18 GB (~16.8 GiB, vision tower included). That leaves ~7 GiB of the 24, minus ~1-1.5 GiB for the CUDA context and compute buffers. - KV cache: Qwen3.8 27B only keeps a KV cache in 16 of its 64 layers (the other 48 are linear attention wit…”

    159753a52 · GitHub · Sep 29, 2026
  9. 9

    “EAGLE/MTP prefix-cache last-block drop causes a 1,648-token recompute per hit on one hybrid Qwen3.8 GDN layout — ~30-40% batch throughput loss on prefix-reusing workloads with speculative decoding enabled Environment - vLLM 0.26.1rc1.dev926+gb05ae5dc0, V1/V2 model runner, async scheduling - Dual RTX 5090 TP=2, FP8 weights + FP8 KV, prefix caching on - Model: Qwen3.8-27B (hybrid: GDN/Mamba layers + full attention → 15 MambaSpec KV groups + full-attention group; scheduler/hash block = 1648 t…”

    Suppressor72 · GitHub · Aug 25, 2026
  10. 10

    “The runs Four models measured under the current protocol, in one batch: | Model | Total | Cost | $/confirmed | Confirmed / reported | | --- | --- | --- | --- | --- | | google/gemini-3.8-flash | 20m12s | $3.33 | $0.303 | 11 / 11 | | anthropic/claude-opus-5 | 27m36s | $10.35 | $0.609 | 17 / 17 | | anthropic/claude-sonnet-5 | 39m51s | $5.81 | $0.726 | 8 / 8 | | anthropic/claude-haiku-4.5 | 10m03s | $0.64 | $0.320 | 2 / 7 | Each cost reconstructed exactly at OpenRouter's published pr…”

    using-system · GitHub · Sep 7, 2026
  11. 11

    “Summary Switch the Cloudflare Worker's LLM from google/gemini-3.6-flash to vertex/gemini-3.8-flash on Requesty. Same output quality on the live batch prompt, half the per-token price, roughly twice as fast, and it never overran the token cap in testing (3.6 Flash did once in four refills, forcing a full strict-prompt retry). Changes (all in worker/) 1. wrangler.toml: set UR_MODEL = "vertex/gemini-3.8-flash". 2. wrangler.toml: bump UR_GENERATION_VERSION from 2 to 3 so dev…”

    alexsiri7 · GitHub · Sep 21, 2026
  12. 12

    “Completed and merged into main in pull request https://github.com/Wladefant/super-board/pull/342 (commit 3c360188711ef811452a2643c9c4265c4ce5db5f). Authoritative deliverable: https://github.com/Wladefant/super-board/blob/3c360188711ef811452a2643c9c4265c4ce5db5f/docs/research/agent-benchmarks-2026-09.md Comprehensive benchmark research delivered: Artificial Analysis Terminal-Bench 4.0 analysis, DeepSeek V4.1 Flash worker tier evaluation ($4.32/solved vs $9.05 on Opus high), cost-per-solve me…”

    Wladefant · GitHub · Sep 29, 2026
  13. 13

    “Same here, and I kind of had this with all ds 4 flash models as well... also running the stock snapshot on SM120 TP4 with engram offloading. This really makes it unreliable to use for us, and we reverted to GLM53 flash now, although I really like the much faster architecture of ds 4/4.1 flash!”

    opt-dev · Hugging Face · Oct 18, 2026
  14. 14

    “Within the 59 catalog models matched to this evaluation: Scores 79.93% on LiveBench Instruction Following (#2 of 59), including paraphrasing, simplifying, story generation, and summarization.”

    LiveBench Instruction Following · Benchmark · Oct 18, 2026
  15. 15

    T-139: LLM多層ルーティング — gemini-flash統一・thinkingエスカレーション・Glimmer切替閾値

    Original-language excerpt:“追加評価: gpt-5.6-luna（2026-08-17） | | gpt-4o-mini | gemini-3.7-flash | gpt-5.6-luna | |---|---|---|---| | 精度 | 5/8 | 7/8 | 7/8 | | 平均レイテンシ | 3.1s | 4.2s | 4.3s | | 単価（in/out per 1M） | $0.15/$0.60（legacy） | $0.75/$3.75 → 2027年に$1.50/$7.50へ値上げ | $0.20/$1.20（値上げ予定なし） | | draft 1回概算 | 〜$0.0007 | 〜$0.004 | 〜$0.0011 | - Luna は gemini-3.7-flash と同じ 7/8（落ちたのは全モデル共通の conflict warnings ケースのみ）。曖昧期限・前日準備も正しく分解 - priority 判定は gemini がやや上（Luna は起床/出社/帰宅に mandatory を付ける過剰傾向。ただし商談=external/…”

    devacxx01 · GitHub · Aug 17, 2026
  16. 16

    “Prices per provider from OpenRouter's list, 23 Sep. Monthly cost assumes 100M input + 20M output tokens. | Provider | $/M input | $/M output | Monthly | |---|---|---|---| | DeepInfra | 0.56 | 2.50 | $106 | | Novita | 0.78 | 2.46 | $127 | | Reka | 0.76 | 2.57 | $127 | | Io Net | 0.81 | 2.73 | $135 | | Phala | 0.84 | 2.64 | $137 | Same model, same usage: $106 at the cheapest provider, $342 at the most expensive. Live prices, precision and uptime for all 31, with a calculator for your own usag…”

    yash-711 · Hugging Face · Sep 23, 2026
  17. 17

    “Observed Z.AI announced GLM-5.3 to all API users on 2026-08-22: same base model as GLM-5.2, same wire shape, "one parameter change" to adopt, and — per the launch note — "available at the same price". #6988 catalogued the route the same day but deliberately left it unpriced ("Z.AI has not added it to the public per-token rate card"). Two problems follow from the state that landed: 1. zai/glm-5.3 carries no pricing. docs.z.ai's rate card now lists GLM-5.3 at $1.40 in / $4.40 out / $…”

    kennethsinder · GitHub · Aug 23, 2026
  18. 18

    “Within the 152 catalog models matched to this evaluation: Ranks #14 of 152 on LMArena's overall text arena (Elo 1487), based on blind human preference votes.”

    LMArena text arena · Benchmark · Oct 15, 2026
  19. 19

    “Corrección: los costos reales, medidos en DevPass Primera facturación real de las lanes de este issue. Mis proyecciones estaban 6-10× por encima. | Rol | Modelo real | Req | Tokens | Costo | | --- | --- | ---: | ---: | ---: | | explorer | azure/gpt-5.6-luna | 5 | 127.2K | $0.0104 | | reviewer | meta/muse-spark-1.3 | 5 | 147.1K | $0.0861 | | (auxiliar) | google-vertex/gemini-3.7-flash | 2 | 3.0K | $0.0023 | | | | 12 | 277.3K | $0.0988 | Las dos lanes completas de…”

    ingamartinez · GitHub · Sep 11, 2026
  20. 20

    “Context Unified spec item 3b — per-action Gemini model routing (economics doc E-2: ~32% blended cost cut, "one URL constant plus a per-action switch, no product change"). Routing: ask/propose → gemini-3.6-flash, hint → gemini-3.5-flash-lite. Both targets are VERIFIED REACHABLE on the prod key — see the 2026-07-28 curl record in #838's comments (AIE-04: all three candidates HTTP 200; AIE-05: thinkingBudget: 0 verified honored with a control). Note the route's line-69 comme…”

    thomgabriel · GitHub · Jul 28, 2026

Rankings synthesized from community evidence and open benchmarks. See methodology. Not driven by vendor marketing.