Cheaper alternatives to Mistral Large 2407
Mistral LMArena 1266.3 $3.00/M on a balanced workload prices as of 2026-08-28
12 models are both better and cheaper, giving up nothing.
Of the 133 models carrying both an independent score and a published price at the same delivery mode, 97 score at least as high as Mistral Large 2407 and cost no more under at least one workload. 93 of them are genuinely cheaper; the rest match the price and win on score alone. 12 match or beat it on every capability we hold — context, maximum output, input modes, tool use and extended reasoning. 85 do not, and every row names what it drops.
Dominance is not a property of a model. It is a property of a model, a capability lens and a workload, and all three are named on every row. Scores are LMArena Elo, used under CC BY 4.0 from the official dataset; prices are blended per million tokens. A higher score is not a drop-in replacement.
This page is built from that comparison and nothing else. When no model is both better and cheaper than Mistral Large 2407, it is not generated.
Where it holds
| Workload | Mistral Large 2407 $/M | Better and cheaper | With a trade |
|---|---|---|---|
| Balanced | $3.00 | 10 | 83 |
| Summarise | $1.69 | 9 | 84 |
| Chat | $3.06 | 7 | 81 |
| Code gen | $4.26 | 7 | 82 |
| Agentic | $1.53 | 7 | 81 |
Workload mixes are defined on the methodology page. Cached input is priced at the cached rate, and reasoning tokens at the worse of the reasoning and output rates.
What you would be replacing
| Intelligence | 1266.3 |
|---|---|
| Context window | 131K |
| Max output | 105K |
| Input modes | text, file |
| Tool use | yes |
| Extended reasoning | no |
A replacement has to clear every line above, not just the score. Full record for Mistral Large 2407.
Better and cheaper, nothing given up 12
Each of these matches or beats Mistral Large 2407 on context, maximum output, input modes, tool use and reasoning, scores at least as high, and costs no more.
| # | Model | Intelligence | $/M | Holds under |
|---|---|---|---|---|
| 1 | Muse Spark 1.1 Meta | 1478.3 +212 | $2.00 |
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| 2 | Grok 4.5 xAI | 1452.3 +186 | $3.00 |
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| 3 | Grok 4.6 xAI | 1443.7 +177.4 | $3.00 |
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| 4 | GPT-5.6 Luna OpenAI | 1428.5 +162.2 | $0.450 |
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| 5 | Mistral Medium 3.5 Mistral | 1420.9 +154.6 | $3.00 |
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| 6 | GPT-5.4 Mini OpenAI | 1412.1 +145.8 | $1.69 |
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| 7 | Grok 4.3 xAI | 1397.5 +131.2 | $1.56 |
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| 8 | GPT-5.4 Nano OpenAI | 1372.8 +106.5 | $0.463 |
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| 9 | GPT-5 Mini OpenAI | 1373.4 +107.1 | $0.688 |
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| 10 | GPT-5 Nano OpenAI | 1320.3 +54 | $0.138 |
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| 11 | GPT-5.1 OpenAI | 1441.4 +175.1 | $1.37 summarise |
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| 12 | GPT-5 OpenAI | 1405.6 +139.3 | $1.37 summarise |
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Cheaper and higher-scoring, but you give something up 85
These score at least as high and cost no more on the two plotted axes, and lose something that is not on them. Read the last column before switching.
| Model | Intelligence | $/M | Holds under | What you give up |
|---|---|---|---|---|
| Gemini 3.7 Flash Google | 1490.2 +223.9 | $0.750 |
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| Qwen3.8 Max Qwen | 1481.9 +215.6 | $3.00 |
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| Gemini 3.6 Flash Google | 1476.5 +210.2 | $1.50 |
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| GLM 5.3 Z.ai | 1476.9 +210.6 | $2.15 |
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| MiMo-V2.5-Pro Xiaomi | 1465 +198.7 | $0.544 |
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| GLM 5.2 Z.ai | 1465.4 +199.1 | $1.83 |
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| GLM 5.1 Z.ai | 1464.1 +197.8 | $1.94 |
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| Qwen3.7 Plus Qwen | 1456.2 +189.9 | $0.560 |
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| Kimi K2.6 Moonshot AI | 1455 +188.7 | $1.71 |
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| Kimi K2.5 Moonshot AI | 1445.2 +178.9 | $0.900 |
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| GLM 5 Z.ai | 1445.2 +178.9 | $0.930 |
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| Gemma 4 31B Google | 1441.7 +175.4 | $0.152 |
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| Hy3 Tencent | 1441.2 +174.9 | $0.144 |
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| Qwen3.6 Max Preview Qwen | 1446.3 +180 | $2.31 |
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| Qwen3.8 27B Qwen | 1440.8 +174.5 | $0.956 |
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| GLM 4.6 Z.ai | 1439.8 +173.5 | $0.875 |
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| DeepSeek V4 Pro 0423 DeepSeek | 1439.2 +172.9 | $1.09 |
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| Qwen3.5 397B A17B Qwen | 1438.3 +172 | $0.877 |
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| Qwen3.6 Plus Qwen | 1436.8 +170.5 | $0.731 |
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| Gemma 4 26B A4B Google | 1434.6 +168.3 | $0.138 |
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| Gemini 3.5 Flash Lite Google | 1436.5 +170.2 | $0.850 |
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| Inkling Thinking Machines | 1439.2 +172.9 | $1.73 |
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| MiniMax M3 MiniMax | 1434.8 +168.5 | $0.525 |
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| GLM 4.7 Z.ai | 1435.3 +169 | $0.738 |
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| DeepSeek V4 Flash 0423 DeepSeek | 1431.6 +165.3 | $0.101 |
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| GLM 5V Turbo Z.ai | 1436.8 +170.5 | $1.90 |
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| MiMo-V2.5 Xiaomi | 1427.3 +161 | $0.175 |
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| GLM 4.5 Z.ai | 1429.4 +163.1 | $1.00 |
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| R1 0528 DeepSeek | 1427.9 +161.6 | $0.912 |
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| DeepSeek V3.2 DeepSeek | 1424.6 +158.3 | $0.290 |
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| DeepSeek V3.2 Exp DeepSeek | 1424.4 +158.1 | $0.305 |
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| Qwen3 VL 235B A22B Instruct Qwen | 1420.9 +154.6 | $0.632 |
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| Qwen3 235B A22B Instruct 2507 Qwen | 1419.3 +153 | $0.205 |
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| DeepSeek V3.1 Terminus DeepSeek | 1419.6 +153.3 | $0.453 |
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| Qwen3 Next 80B A3B Instruct Qwen | 1418.6 +152.3 | $0.350 |
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| DeepSeek V3.1 DeepSeek | 1419.1 +152.8 | $0.825 |
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| Qwen3.5-122B-A10B Qwen | 1417.9 +151.6 | $0.715 |
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| Gemini 2.5 Flash Google | 1417.3 +151 | $0.850 |
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| Gemini 3.1 Flash Lite Preview Google | 1414.8 +148.5 | $0.563 |
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| Qwen3 235B A22B Thinking 2507 Qwen | 1413.8 +147.5 | $0.748 |
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| Inkling Small Thinking Machines | 1411.7 +145.4 | $0.637 |
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| Qwen3 Max Qwen | 1412.7 +146.4 | $1.56 |
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| Qwen3.5-27B Qwen | 1407.9 +141.6 | $0.536 |
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| MiniMax M2.7 MiniMax | 1405.3 +139 | $0.525 |
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| Step 3.5 Flash StepFun | 1403.8 +137.5 | $0.150 |
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| Qwen3.5-Flash Qwen | 1397.6 +131.3 | $0.114 |
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| Qwen3 VL 235B A22B Thinking Qwen | 1400.6 +134.3 | $1.30 |
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| Qwen3.5-35B-A3B Qwen | 1395.6 +129.3 | $0.500 |
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| Claude Haiku 4.5 Anthropic | 1394.9 +128.6 | $2.00 |
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| Qwen3 30B A3B Instruct 2507 Qwen | 1384.3 +118 | $0.084 |
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| GLM 4.5 Air Z.ai | 1382.8 +116.5 | $0.310 |
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| Solar Pro 4 Upstage | 1376.2 +109.9 | $0.052 |
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| DeepSeek V3 0324 DeepSeek | 1375 +108.7 | $0.438 |
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| GLM 4.6V Z.ai | 1374.7 +108.4 | $0.450 |
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| R1 DeepSeek | 1372.7 +106.4 | $1.15 |
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| Qwen3 Next 80B A3B Thinking Qwen | 1367.5 +101.2 | $0.412 |
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| gpt-oss-120b OpenAI | 1365.6 +99.3 | $0.070 |
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| Qwen3 235B A22B Qwen | 1366 +99.7 | $0.796 |
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| Nova 2 Lite Amazon | 1362.2 +95.9 | $0.850 |
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| Gemma 3 27B Google | 1358.3 +92 | $0.172 |
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| MiniMax M2.5 MiniMax | 1359 +92.7 | $0.472 |
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| Mercury 2 Inception | 1357.8 +91.5 | $0.375 |
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| GLM 4.7 Flash Z.ai | 1352.9 +86.6 | $0.145 |
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| o4 Mini OpenAI | 1352.5 +86.2 | $1.93 |
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| Trinity Large Thinking Arcee | 1341.9 +75.6 | $0.378 |
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| MiniMax M2 MiniMax | 1342.1 +75.8 | $0.446 |
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| Qwen3 32B Qwen | 1340.1 +73.8 | $0.130 |
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| MiniMax M1 MiniMax | 1341.9 +75.6 | $0.963 |
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| GPT-4.1 Mini OpenAI | 1340.5 +74.2 | $0.700 |
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| Gemma 3 12B Google | 1334.2 +67.9 | $0.075 |
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| DeepSeek V3 DeepSeek | 1332.6 +66.3 | $0.450 |
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| GLM 4.5V Z.ai | 1333.6 +67.3 | $0.900 |
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| o3 Mini High OpenAI | 1336.6 +70.3 | $1.93 |
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| Qwen3 30B A3B Qwen | 1316.9 +50.6 | $0.215 |
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| o3 Mini OpenAI | 1319.2 +52.9 | $1.93 |
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| Olmo 3 32B Think Allen AI | 1298.5 +32.2 | $0.237 |
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| Granite 4.1 8B IBM | 1291.6 +25.3 | $0.063 |
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| Gemma 3 4B Google | 1290.8 +24.5 | $0.063 |
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| gpt-oss-20b OpenAI | 1287.8 +21.5 | $0.055 |
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| GPT-4o-mini (2024-07-18) OpenAI | 1286.6 +20.3 | $0.263 |
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| GPT-4.1 Nano OpenAI | 1284.8 +18.5 | $0.175 |
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| Llama 3.3 70B Instruct Meta | 1274.9 tie | $0.710 |
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| Qwen2.5 72B Instruct Qwen | 1269.1 tie | $0.370 |
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| Gemini 3.5 Flash Google | 1482.6 +216.3 | $1.49 summarise |
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| Gemini 2.5 Pro Google | 1457.3 +191 | $1.37 summarise |
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What this compares, and what it leaves out
- Quality is LMArena Elo, used under CC BY 4.0 from the official dataset. The 95% confidence interval on a difference between two scores is about ±10.68 points, so a gap smaller than that is marked tie rather than an improvement — see significance bands.
- 188 further models at this delivery mode carry a price but no independent score. They are absent from the comparison in both directions — unrated is not a zero, and an unmeasured model is neither an alternative nor a worse buy.
- Retired models are never offered as an alternative, and a model only competes against its own delivery mode: batch trades latency for price, so it is not a like-for-like swap.
- Nothing here measures latency, throughput, rate limits or how a model behaves on your prompts. Two models with the same index score are not interchangeable.
- The models that nothing beats on both axes are on the value frontier, and every other model something cheaper beats is listed here.