Recommendation for Math / Reasoning
Math & Reasoning
Our top recommendation for Math & Reasoning, based on the public evidence we track, is Anthropic: Claude Fable 5.[1] OpenAI: GPT-5.6 Sol is the next-ranked alternative. Deploy for high-stakes mathematics where LiveBench's objective scoring validates strong competition performance.
About this recommendation
- Updated
- Sep 25, 2026
- Evidence through
- Sep 25, 2026
- Sources
- 10
- Revision
- v78
Decision audit
Why this result
Inspect the inputs and the computed order behind the recommendation.
Models screened
21
live candidates
Evaluation feeds
5
task-weighted
Winner coverage
100%
intended feed weight
Largest provider share
3 of 6
Anthropic
Sources evaluated
The task sets these weights before any model is scored.
| Evaluation feed | Weight | Winner result | Field measured |
|---|---|---|---|
| LiveBench Mathematics | 35% | #6 | 21/21 |
| LiveBench Reasoning | 25% | #10 | 21/21 |
| LMArena Math | 25% | #3 | 19/21 |
| LMArena Instruction Following | 10% | #4 | 20/21 |
| OpenRouter usage | 5% | 79/100 | 21/21 |
Provider concentration
Each exact model is scored separately; provider identity is not a ranking input.
- Anthropic3 models
- Google1 model
- OpenAI1 model
- Qwen1 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.
| Rank | Model | Relative score | Coverage | Practitioner evidence | Strongest measured reason |
|---|---|---|---|---|---|
| 01 | Claude Fable 5Anthropic | 85 | 100% | 1 threads · 1 families · 0 cautions | #3 LMArena Math · #4 LMArena Instruction Following |
| 02 | GPT-5.6 SolOpenAI | 82 | 100% | 1 threads · 1 families · 1 cautions | #4 LiveBench Reasoning · #5 LiveBench Mathematics |
| 03 | Claude Opus 4.6Anthropic | 81 | 100% | 2 threads · 1 families · 0 cautions | #1 LMArena Instruction Following · #5 LMArena Math |
| 04 | Qwen3.8 27BQwen | 75 | 100% | 5 threads · 2 families · 0 cautions | #25 LMArena Math · #37 LMArena Instruction Following |
| 05 | Gemini 3.6 FlashGoogle | 73 | 100% | no linked practitioner threads | #8 LMArena Math · #18 LMArena Instruction Following |
| 06 | Claude Opus 5.5Anthropic | 63 | 44% | no linked practitioner threads | #1 LiveBench Mathematics · #2 LiveBench Reasoning |
Relative score combines normalized benchmark quality and signal coverage; independent practitioner evidence and freshness are bounded tie-breakers. It is an ordering score, not an absolute quality percentage. The writing model receives this order and cannot change it.
Anthropic: Claude Fable 5 ranks #4 of 139 on LMArena's maths category (Elo 1522), based on blind human preference votes for maths prompts.
Best when: Consider only after reviewing the cited caution.
Scores 96.2% on LiveBench Mathematics (#5 of 58) and ranks #13 overall on LMArena, with documented numerical precision issues in optimization code.
Best when: Deploy for high-stakes mathematics where LiveBench's objective scoring validates strong competition performance.
Tips
- Deploy for high-stakes mathematics where LiveBench's objective scoring validates strong competition performance.
Watch out for
- Watch for float32 underflow in numerical optimization, specifically when multiplying small gradient terms by Armijo conditions before reduction.
Ranks #5 on LMArena's maths category and demonstrates practical simulation coding ability, though LiveBench Reasoning places it at #16 of 58.
Best when: Use for physics simulation and orbital mechanics code where users report success despite limited formal math background.
Tips
- Use for physics simulation and orbital mechanics code where users report success despite limited formal math background.
Watch out for
- Expect reasoning traces to be hidden or truncated, as Anthropic has moved toward concealing chain-of-thought outputs.
An open-weight 27B model with verified functional correctness on math and 60K context retrieval, though LMArena maths ranking sits at #26 of 139.
Best when: Run locally on RTX 5090 with NVFP4 compression for ~1.6x faster decode, accepting a small quality tax versus Q4_K_M GGUF.
Tips
- Run locally on RTX 5090 with NVFP4 compression for ~1.6x faster decode, accepting a small quality tax versus Q4_K_M GGUF.
- Deploy for hard problems where benchmark standings include GPQA Diamond 89.2 and LiveCodeBench v6 90.3.
Watch out for
- Tune MTP (Multi-Token Prediction) settings carefully, as configuration choices significantly impact latency and output quality on hard problems.
Ranks #7 on LMArena's maths category but scores 85.15% on LiveBench Reasoning, placing #30 of 58 on objective ground-truth tasks.
Best when: Consider for math tasks where human preference rankings matter more than objective reasoning benchmarks.
Tips
- Consider for math tasks where human preference rankings matter more than objective reasoning benchmarks.
Leads the LiveBench Mathematics benchmark at 97.08%, placing first among 58 models on competition and olympiad-style tasks.
Best when: Use for competition math and olympiad problems where objective scoring confirms top-tier performance.
Tips
- Use for competition math and olympiad problems where objective scoring confirms top-tier performance.
Frequently asked
- What is an alternative to Anthropic: Claude Fable 5?
- OpenAI: GPT-5.6 Sol is the next-ranked option. Deploy for high-stakes mathematics where LiveBench's objective scoring validates strong competition performance.[1]
Sources
- 1
“Scores 96.2% on LiveBench Mathematics (#5 of 58), using objectively scored competition and olympiad-style tasks.”
LiveBench Mathematics · Benchmark · Jun 25, 2026 - 2
“INDEPENDENT NUMERICAL REVIEW · Z-Switch-Q9V3 / GPT-5.6 Sol · issue #15960 Fresh review found a blocker in the earlier proposed "scale before reduce" repair: it fixes the original overflow witness but can underflow small Armijo terms to zero and accept an insufficiently improving step. Reproduced float32 witness: 200 coordinates, g_i=1e-35, d_i=-7, armijo=1e-4, step=1. The mathematically required Armijo decrease is nonzero, but multiplying g by armijo first flushes the per-coordinate terms to ze…”
woahwhattheheck · GitHub · Sep 18, 2026 - 3
“> It's a pretty high moat getting into stuff like simulation software I'm currently working on a simulation game about space and orbital mechanics. I have a lot of software experience, I know how to build large projects and architect my code, and I know how to to test the end result to ensure I'm getting what I want. But I also don't have a strong math or physics background. In my experience, Claude (Opus 4.6+) has had no issues writing any simulation or game related math code. And the key thin…”
pyth0 · Hacker News · Jun 7, 2026 - 4
“The initial motivation for this was likely to thwart any competition. Already Anthropic has accused some companies of organized distillation efforts at a massive scale. Back when I used antigravity, it used to show the reasoning intact - at least for Gemini Pro 3.1, and likely for Claude Opus 4.6 (not 100% certain about it). I have some recollection of stopping the models mid-turn when they started going astray. As a power user, I find reasoning fascinating to read and genuinely useful at times…”
sheepscreek · Hacker News · Jun 23, 2026 - 5
“Hardware: 1xRTX 5090 32 GB (SM 12.0), driver 595.84, CUDA 13.2 toolkit Engine: vLLM 0.27.1 (pip) + FlashInfer 0.6.17, torch 2.13.0+cu130 Model: Qwen3.8-27B, compressed-tensors NVFP4 (W4A16, group 16), MTP speculative head in BF16 ### Quality functional correctness verified (math, tool calling, 60K context retrieval, multi-turn); NVFP4 carries a small quality tax vs GGUF Q4_K_M on evals (independent measurement: q_avg 92.5 vs 93.2, HumanEval 89.6 vs 94.5) — the trade for ~1.6x faster decode than…”
marco9899 · Hugging Face · Aug 18, 2026 - 6
“**Standing issue — intentionally left open. Revisit periodically; close only if the model is ruled out for good.** ## What this tracks Whether [Qwen3.8-27B](https://huggingface.co/Qwen/Qwen3.8-27B) should replace Gemma 4 26B-A4B as the local model, and what would have to change upstream for that to make sense. ## Why it was not adopted on release (2026-08-15) The benchmarks are excellent — GPQA Diamond 89.2, LiveCodeBench v6 90.3, Terminal-Bench 2.1 73.0, SWE-bench Pro 61.7, Apache 2.0, 262K na…”
nbramia · GitHub · Aug 15, 2026 - 7
“A while back I posted a comparison of Qwen3.8-27B against Nemotron 3.5 Lightning and Muse Glimmer on 16 hard problems. Several people asked what settings I was running, and it turned out I did not have a good answer. So I went back and benchmarked the settings themselves: 45 configurations of this model alone.  Sharing the findings here because a few of them c…”
laxmimerit · Hugging Face · Aug 16, 2026 - 8
“ I ran Qwen3.8-27B against two other models in the same size class on a set of 16 hard problems, and wanted to share the numbers here because a few of them surprised me. ### Setup - One RTX 5090, 32GB - Ollama 0.32.12, tag `qwen3.8:latest` (Q4_K_M GGUF, 27.3B) - 64K context, temperature 0.2, generation cap 32,768 tokens - Every model loaded alone on the GPU, with all other…”
laxmimerit · Hugging Face · Aug 16, 2026 - 9
“Ranks #7 of 139 on LMArena's maths category (Elo 1506), based on blind human preference votes for maths prompts.”
LMArena maths category · Benchmark · Sep 13, 2026 - 10
“Scores 97.08% on LiveBench Mathematics (#1 of 58), using objectively scored competition and olympiad-style tasks.”
LiveBench Mathematics · Benchmark · Jun 25, 2026
Rankings synthesized from community evidence and open benchmarks. See methodology. Not driven by vendor marketing.