Updated November 13, 2026. Rank says GLM-5.1 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
GLM-5.1 has the higher public point estimate, 55.47 versus 33.33. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
GLM-5.1: $1.40 input / $4.40 output
Mistral Large 3: $0.50 input / $1.50 output
GLM-5.1: 203K
Mistral Large 3: 256K
GLM-5.1: April 7, 2026
Mistral Large 3: December 2, 2025
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Code generation, repair, and software-engineering tasks
GLM-5.1
GLM-5.1 has the higher public coding point estimate, 46.2 to 15.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Mistral Large 3
Mistral Large 3 has the larger documented context window.
1K fresh input + 500 output tokens
Mistral Large 3
Mistral Large 3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Mistral Large 3
Mistral Large 3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GLM-5.1 and Mistral Large 3 are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate. Mistral Large 3 has no published cached-input rate, so cached tokens use its listed input rate.
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
Like-for-like · BenchAlign v5.8
GLM-5.1 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
LiveCodeBench (Vals)Coding
Normalized gap 26.1MMLU-Pro (Vals)Knowledge
Normalized gap 7.1Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
| Category | GLM-5.1 | Mistral Large 3 | Basis | Reading |
|---|---|---|---|---|
| Coding | 46.2Supported · #55/162 | 15.7Supported · #143/162 | Like-for-likeBenchAlign v5.8 lane · 8 vs 2 public rows | GLM-5.1 leads |
| Knowledge | 52.0Supported · #69/180 | 32.3Supported · #142/180 | Like-for-likeBenchAlign v5.8 lane · 4 vs 2 public rows | GLM-5.1 leads · intervals overlap |
| Agentic | 44.0Estimated · #63/135 | 18.9Estimated · #121/135 | Directional onlyBenchAlign v5.8 lane · 10 vs 0 public rows | Directional only |
| Reasoning | 44.6Estimated · #73/160 | 25.5Estimated · #134/160 | Directional onlyBenchAlign v5.8 lane · 0 vs 0 public rows | Directional only |
| Instruction following | 93.8#7/157 | 41.5#122/157 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Multimodal | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 55.7#8/12 | Not ranked | Not comparableProvisional lane · 3 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
1K fresh input + 500 output tokens
Mistral Large 3 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Mistral Large 3 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate. Mistral Large 3 has no published cached-input rate, so cached tokens use its listed input rate.
Enter a month of tokens to price it at both models’ listed API rates.
Standard listed rates, no batch or volume discounts.
Overall score against output price per 1M tokens, one model per family. Pricing as of November 12, 2026.
GLM-5.1Mistral Large 3
Gray dots are the other priced models. The dark step line is the efficiency frontier: for a model under it, another model costs no more and scores at least as high.
See Mistral Large 3 on the full price vs performance chartSourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
GLM-5.1
203K
Mistral Large 3
256K
GLM-5.1
Not sourced
Mistral Large 3
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5.1
Not published
Mistral Large 3
Not published
GLM-5.1
Mistral Large 3
GLM-5.1
Mistral Large 3
GLM-5.1
Not sourced
Mistral Large 3
Not sourced
GLM-5.1
Reasoning
Mistral Large 3
Non-Reasoning
GLM-5.1
Open Weight
Mistral Large 3
Open Weight
GLM-5.1
Open Weight
Mistral Large 3
Open Weight
GLM-5.1
2026-04-07
Mistral Large 3
2025-12-02
GLM-5.1 has the higher public point estimate, 55.47 versus 33.33. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
GLM-5.1 has the higher public coding point estimate, 46.2 to 15.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GLM-5.1 scores higher for agentic tasks on the public lane, 44 to 18.9. GLM-5.1 and Mistral Large 3 are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
For the stated presets, chat costs $0.0036 on GLM-5.1 and $0.00125 on Mistral Large 3; repository review costs $0.0832 and $0.0295; the cache-heavy agent loop costs $0.352 and $0.125. GLM-5.1 does not fit this workload in one request. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate. Mistral Large 3 has no published cached-input rate, so cached tokens use its listed input rate.
Mistral Large 3 has the larger documented context window: 256K, compared with 203K.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
BrowseComp
Not directly comparable
τ³-bench results
Not directly comparable
MCP Atlas
Not directly comparable
CyberGym
Not directly comparable
Claw-Eval
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
HLE w/ tools
Not directly comparable
SWE-bench Pro
Not directly comparable
NL2Repo
Not directly comparable
SWE-Rebench
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
OpenHarmony Bench v1.0
Not directly comparable
LiveCodeBench (Vals)
GLM-5.1 leads this result
SWE-bench (Vals)
GLM-5.1 leads this result
AIME26
Not directly comparable
HMMT Nov 2025
Not directly comparable
HMMT Feb 2026
Not directly comparable
MMAnswerBench
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
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Last updated November 13, 2026
