☁️ AI Weather Report — Top 10 Models for Coding Value — July 21, 2026

Welcome to the AI Weather Report for July 21, 2026. This daily report ranks the top 10 AI models for coding by bang for the buck — a combination of raw coding capability and API pricing.

📊 Today’s Top 10 Rankings

#ModelProviderCapabilityCost /M tokensValue Score
🥇 1 mistral-nemo mistralai 62/100 $0.0272 2275.2
🥈 2 ling-2.6-flash inclusionai 56/100 $0.0250 2240.0
🥉 3 l3-lunaris-8b sao10k 58/100 $0.0475 1221.1
4 mistral-small-24b-instruct-2501 mistralai 72/100 $0.0725 993.1
5 llama-3.1-8b-instruct meta-llama 62/100 $0.0725 855.2
6 mythomax-l2-13b gryphe 48/100 $0.0600 800.0
7 gpt-oss-20b openai 78/100 $0.1050 742.9
8 qwen-2.5-7b-instruct qwen 60/100 $0.0850 705.9
9 laguna-xs-2.1 poolside 72/100 $0.1050 685.7
10 gpt-oss-120b openai 93/100 $0.1368 680.1

📈 Analysis

🏆 Best Value Today: mistral-nemo scores 2275.2 with a capability rating of 62 at $0.0272/M tokens.

💵 Cheapest Premium Model: ling-2.6-flash at $0.0250/M tokens (capability: 56).

What “Value Score” means: Capability score (based on SWE-bench, HumanEval, LiveCodeBench) divided by blended cost per million tokens (25% input + 75% output weights for coding workloads). Free tier models get a massive boost. Higher is better.

📋 All Scored Models (67 total)

#ModelProviderCapabilityCost /M tokValue
1mistral-nemomistralai62$0.02722275.2
2ling-2.6-flashinclusionai56$0.02502240.0
3l3-lunaris-8bsao10k58$0.04751221.1
4mistral-small-24b-instruct-2501mistralai72$0.0725993.1
5llama-3.1-8b-instructmeta-llama62$0.0725855.2
6mythomax-l2-13bgryphe48$0.0600800.0
7gpt-oss-20bopenai78$0.1050742.9
8qwen-2.5-7b-instructqwen60$0.0850705.9
9laguna-xs-2.1poolside72$0.1050685.7
10gpt-oss-120bopenai93$0.1368680.1
11gemma-3-4b-itgoogle50$0.0875571.4
12deepseek-v4-flashdeepseek91$0.1641554.4
13granite-4.1-8bibm-granite48$0.0875548.6
14qwen3.5-9bqwen72$0.1375523.6
15gemma-3-12b-itgoogle60$0.1250480.0
16command-r7b-12-2024cohere54$0.1219443.1
17granite-4.0-h-microibm-granite38$0.0882430.6
18ministral-3b-2512mistralai42$0.1000420.0
19nova-micro-v1amazon45$0.1137395.6
20hy3-previewtencent68$0.1732392.5
21qwen3-32bqwen88$0.2300382.6
22qwen3-coder-30b-a3b-instructqwen84$0.2200381.8
23qwen3.5-flash-02-23qwen70$0.2112331.4
24qwen3-30b-a3b-instruct-2507qwen82$0.2500328.0
25gpt-oss-safeguard-20bopenai77$0.2437315.9
26mistral-small-3.2-24b-instructmistralai78$0.2500312.0
27nemotron-3-nano-30b-a3bnvidia50$0.1625307.7
28nova-lite-v1amazon58$0.1950297.4
29gemma-3-27b-itgoogle68$0.2500272.0
30gemma-4-26b-a4b-itgoogle72$0.2725264.2
31seed-1.6-flashbytedance-seed64$0.2437262.6
32gpt-5-nanoopenai82$0.3125262.4
33llama-3.3-70b-instructmeta-llama84$0.3325252.6
34gemma-4-31b-itgoogle74$0.3075240.7
35step-3.5-flashstepfun60$0.2500240.0
36nemotron-3-super-120b-a12bnvidia76$0.3212236.6
37laguna-m.1poolside80$0.3500228.6
38seed-2.0-minibytedance-seed72$0.3250221.5
39qwen3-235b-a22b-2507qwen96$0.4350220.7
40llama-3.1-70b-instructmeta-llama82$0.4000205.0
41llama-3.2-1b-instructmeta-llama30$0.1575190.5
42glm-4.7-flashz-ai60$0.3151190.4
43gpt-4.1-nanoopenai60$0.3250184.6
44llama-3.2-3b-instructmeta-llama48$0.2640181.8
45ring-2.6-1tinclusionai78$0.4875160.0
46qwen3-next-80b-a3b-thinkingqwen93$0.6094152.6
47gpt-4o-miniopenai74$0.4875151.8
48ling-2.6-1tinclusionai74$0.4875151.8
49qwen3-next-80b-a3b-instructqwen90$0.6094147.7
50deepseek-chatdeepseek90$0.6501138.4
51command-r-08-2024cohere60$0.4875123.1
52qwen3-coderqwen85$0.8250103.0
53qwen-2.5-coder-32b-instructqwen86$0.915094.0
54hermes-3-llama-3.1-405bnousresearch78$1.0078.0
55claude-3-haikuanthropic72$1.0072.0
56dolphin-mistral-24b-venice-editioncognitivecomputations52$0.725071.7
57gpt-4.1-miniopenai76$1.3058.5
58deepseek-r1deepseek95$2.0546.3
59gemini-2.5-flashgoogle86$1.9544.1
60nova-pro-v1amazon70$2.6026.9
61gpt-4.1openai90$6.5013.8
62gpt-5openai97$7.8112.4
63gemini-2.5-progoogle94$7.8112.0
64gpt-4oopenai88$8.1310.8
65command-r-plus-08-2024cohere68$8.138.4
66claude-sonnet-4anthropic96$12.008.0
67claude-opus-4anthropic98$60.001.6

Generated 2026-07-21 02:00 UTC · Data from OpenRouter API and public benchmarks · Bang-for-Buck = Capability / Cost