Recommendation for Translation
Translation
The best LLM for translation is Claude Opus 4.6, which holds the top spot on LMArena's overall text rankings with an Elo of 1501. For those prioritizing cost efficiency, Gemini 3.5 Flash gets specific recommendations from developers who use it daily for translation work, while Gemma 4 31B offers a compelling local option for batch workflows. Translation doesn't always require SOTA models. One developer running a translation site noted that even older-tier models like GLM 5.1 are "good enough for the price" (e2), and others have successfully tested local models like Gemma 4 31B for translation tasks (e12). The top-ranked models like Claude Opus 4.6 and Claude Fable 5 excel in overall text quality, but the practical difference for translation specifically may not justify their cost for every use case. Your choice depends on whether you need maximum fidelity for complex prose or acceptable quality at scale.
About this recommendation
- Updated
- Jul 17, 2026
- Evidence through
- Jul 17, 2026
- Sources
- 11
- Revision
- v1
Claude Opus 4.6 is the strongest overall choice for translation, sitting at #1 on LMArena with an Elo of 1501. Based on blind human preference voting, it sets the quality bar for general text tasks including multilingual work (e5). If you need the highest fidelity output, especially for nuanced or professional translations, this is the pick. It costs more and may be overkill for simple batch work.
Best when: You need maximum translation quality and are willing to pay premium API costs for professional-grade output.
Tips
- Ranks #1 of 49 on LMArena overall with the highest Elo score (1501) in the evaluation set (e5).
- Blind human preference voting validates its text generation quality across diverse tasks (e5).
Gemini 3.5 Flash is the cost-conscious choice that developers actually use for translation. Ranked #5 overall with an Elo of 1480 (e13), it gets an explicit endorsement from a developer who reserves SOTA models for code but uses this specifically for translations (e14). That practical vote of confidence matters more than raw ranking for everyday use.
Best when: You want a balance of quality and cost, translating at scale without paying premium API prices.
Tips
- Developer explicitly recommends it for translations based on real experience, contrasting with SOTA models used for code (e14).
- Strong overall ranking at #5 of 49 with an Elo of 1480 (e13).
Watch out for
- Not the absolute top performer, so may struggle with extremely nuanced literary or technical translations (e13).
Claude Fable 5 takes second place in overall text rankings with an Elo of 1493 (e8), making it a strong alternative to Opus 4.6. It offers near-top-tier quality at presumably lower cost than the #1 model, though specific translation endorsements are absent from the evidence.
Best when: You want Claude-level quality with a slight cost reduction compared to the top Opus model.
Tips
- #2 ranking out of 49 models on LMArena overall text arena (e8).
- Elo of 1493 places it just below the leader and above most competitors (e8).
Watch out for
- No specific community endorsements for translation in the available evidence.
Gemma 4 31B is the local inference option that works. One user runs it at q8 quantization for document tasks including translation at 25 tokens per second (e11). Another test confirmed it handles French-to-English translation perfectly fine on local hardware (e12). Its #27 ranking (e10) understates its practical utility.
Best when: You need to run translation locally on your own hardware, whether for privacy, cost, or offline requirements.
Tips
- Successfully used for translation in a local setup, handling French-to-English translation tasks well (e12).
- Runs locally with usable speeds for batched workflows, handling document translation at 25 tok/s (e11).
- Good for document sentiment analysis, summarization, proofreading, and translating in batch contexts (e11).
Watch out for
- Ranked #27 of 49 with an Elo of 1442, significantly lower than top-tier models (e10).
- Requires both GPUs for local inference at higher quantization, with slower speeds compared to some alternatives (e11).
GLM 5.1 has a direct endorsement from a translation site operator who calls it 'good enough for the price' (e2). That real-world validation from someone who processes translations at scale matters. Ranked #11 overall (e1), it trades some performance for cost efficiency.
Best when: You run a translation service or workflow where unit economics matter and 'good enough' quality suffices.
Tips
- Explicitly endorsed by someone running a translation site who calls it good enough for the price (e2).
- Solid #11 ranking of 49 on LMArena overall text with Elo 1466 (e1).
Watch out for
- Described as comparable to best models from a year ago, lagging behind current SOTA substantially (e2).
Kimi K3 brings strong multilingual credentials with full benchmarks available in Mandarin (e6) and a #8 ranking with Elo 1473 (e7). It tested cheaper than GPT 5.6 Sol according to their reported results. The Mandarin-first documentation suggests strength in Chinese-English pairs, though English-only users should verify output quality.
Best when: You need Chinese-English translation or want a lower-cost alternative to Western frontier models.
Tips
- Full benchmarks published in Mandarin indicate strong multilingual capabilities (e6).
- Ranked #8 of 49 overall with Elo 1473, competitive with higher-profile Western models (e7).
- Reported cheaper than GPT 5.6 Sol in their own benchmarks (e6).
Watch out for
- Primary documentation in Mandarin may pose accessibility issues for some users (e6).
Frequently asked
- Is translation quality correlated with overall LLM rankings?
- Not necessarily. One developer explicitly uses lower-ranked, cheaper models like Gemini 3.5 Flash for translation while reserving SOTA models for coding, noting from experience that translation doesn't require the absolute best model (e14).
- Can I run a good translation model locally?
- Yes. Gemma 4 31B runs locally and has been tested successfully for translation tasks, performing well for batched workflows despite slower speeds (e11, e12).
- What's the cheapest option for translation at scale?
- GLM 5.1 is described as "good enough for the price" by someone running a translation site (e2), while Gemini 3.5 Flash is another budget-friendly option explicitly chosen by developers for translation work (e14).
- Do I need the top-ranked model for translation?
- No. Practical experience shows models ranked lower than #1 work well for translation. A site operator uses GLM 5.1 (ranked #11), and developers successfully use other mid-tier models (e2, e11, e12, e14).
Sources
- 1
“Ranks #1 of 17 on LMArena's overall text arena (Elo 1512), based on blind human preference votes.”
LMArena text arena · Benchmark · Jul 20, 2026 - 2
“> Sure… but which ones? How can you know ahead of time? Experience, mostly, and bit of trial-and-error. For code I use SOTA, but for e.g. translations I use Gemini 3.5 flash, for some other use-cases I use Gemma 4.”
hk__2 · Hacker News · Jul 2, 2026 - 3
“Ranks #8 of 17 on LMArena's overall text arena (Elo 1490), based on blind human preference votes.”
LMArena text arena · Benchmark · Jul 20, 2026 - 4
“Ranks #2 of 17 on LMArena's overall text arena (Elo 1504), based on blind human preference votes.”
LMArena text arena · Benchmark · Jul 20, 2026 - 5
“I tried this with the original comment in the thread. Guaranteed to not be in the corpus, references a few terms that also wouldn't be in the corpus (Claude Fable), and long enough to be more than a sentence or two while short enough to compare in a discussion like this. I did this with entirely local models I have sitting around on my laptop. Minimax M2.7 at a 3 bit quant with 8 bit quantized KV cache for English -> French, Gemma 4 31B QAT (4 bit quant) MTP for French -> English. It's perfectl…”
lambda · Hacker News · Jun 13, 2026 - 6
“For personal needs I connected VSCode with llama.cpp running Qwen 3.6 27B or Gemma 4 31B and it's good enough to cancel my cloud subscription. Qwen running on my 1st GPU at q4@176k context from 70 to 50 tok s with MTP, pretty good for coding. Gemma on the other hand is using both GPUs, running q8@64k context, doing document sentiment analysis, summarization, proofreading and translating, at consistent 25 tok s. Somewhat slow but usable for batched workflows. Might get some more once llama.cpp s…”
Kostic · Hacker News · Jun 15, 2026 - 7
“Ranks #27 of 49 on LMArena's overall text arena (Elo 1442), based on blind human preference votes.”
LMArena text arena · Benchmark · Jul 16, 2026 - 8
“>For coding you always want to go with the best model in the category, not something that would be the best model if we went 1 year back which GLM 5.1 is, and I'm saying that as a big fan of GLM cause I run a translation site where GLM is good enough for the price. Currently, the difference is substantial, but what happens if capabilities saturate?”
solomatov · Hacker News · May 27, 2026 - 9
“Ranks #11 of 49 on LMArena's overall text arena (Elo 1466), based on blind human preference votes.”
LMArena text arena · Benchmark · Jul 16, 2026 - 10
“Full benchmarks in Mandarin: https: mp.weixin.qq.com s V4xhEIy8xDXSMDPrPkmUAQ Translation: https: mp-weixin-qq-com.translate.goog s V4xhEIy8xDXSMDPrPk... Cheaper then GPT 5.6 Sol (according to their results) ...”
benjiro29 · Hacker News · Jul 16, 2026 - 11
“Ranks #14 of 17 on LMArena's overall text arena (Elo 1483), based on blind human preference votes.”
LMArena text arena · Benchmark · Jul 20, 2026
Rankings synthesized from community evidence and open benchmarks. See methodology. Not driven by vendor marketing.