Oct 08

☁️ AI Weather Report — Top 10 Models for Coding Value — October 09, 2026

Welcome to the AI Weather Report for October 09, 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 $0.0297 2084.0
🥈 2 l3-lunaris-8b sao10k 58/100 $0.0475 1221.1
🥉 3 gpt-oss-20b openai 78/100 $0.0720 1083.3
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 laguna-xs-2.1 poolside 72/100 $0.1050 685.7
7 gpt-oss-120b openai 93/100 $0.1368 680.1
8 gemma-3-4b-it google 50/100 $0.0875 571.4
9 qwen3.5-9b qwen 72/100 $0.1375 523.6
10 gemma-3-12b-it google 60/100 $0.1250 480.0

📈 Analysis

🏆 Best Value Today: mistral-nemo scores 2084.0 with a capability rating of 62 at $0.0297/M tokens.

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 (60 total)

#ModelProviderCapabilityCost /M tokValue
1mistral-nemomistralai62$0.02972084.0
2l3-lunaris-8bsao10k58$0.04751221.1
3gpt-oss-20bopenai78$0.07201083.3
4mistral-small-24b-instruct-2501mistralai72$0.0725993.1
5llama-3.1-8b-instructmeta-llama62$0.0725855.2
6laguna-xs-2.1poolside72$0.1050685.7
7gpt-oss-120bopenai93$0.1368680.1
8gemma-3-4b-itgoogle50$0.0875571.4
9qwen3.5-9bqwen72$0.1375523.6
10gemma-3-12b-itgoogle60$0.1250480.0
11mythomax-l2-13bgryphe48$0.1025468.3
12command-r7b-12-2024cohere54$0.1219443.1
13granite-4.0-h-microibm-granite38$0.0882430.6
14ministral-3b-2512mistralai42$0.1000420.0
15nova-micro-v1amazon45$0.1137395.6
16qwen3-32bqwen88$0.2300382.6
17mistral-small-3.2-24b-instructmistralai78$0.2109369.8
18qwen3-coder-30b-a3b-instructqwen84$0.2275369.2
19qwen-2.5-7b-instructqwen60$0.1750342.9
20gemma-4-26b-a4b-itgoogle72$0.2104342.2
21qwen3.5-flash-02-23qwen70$0.2112331.4
22qwen3-30b-a3b-instruct-2507qwen82$0.2500328.0
23gpt-oss-safeguard-20bopenai77$0.2437315.9
24nova-lite-v1amazon58$0.1950297.4
25gemma-4-31b-itgoogle74$0.2775266.7
26seed-1.6-flashbytedance-seed64$0.2437262.6
27gpt-5-nanoopenai82$0.3125262.4
28nemotron-3-nano-30b-a3bnvidia50$0.1950256.4
29step-3.5-flashstepfun60$0.2500240.0
30seed-2.0-minibytedance-seed72$0.3250221.5
31qwen3-235b-a22b-2507qwen96$0.4350220.7
32nemotron-3-super-120b-a12bnvidia76$0.3575212.6
33llama-3.1-70b-instructmeta-llama82$0.4000205.0
34llama-3.3-70b-instructmeta-llama84$0.4300195.3
35llama-3.2-1b-instructmeta-llama30$0.1575190.5
36glm-4.7-flashz-ai60$0.3151190.4
37gemma-3-27b-itgoogle68$0.3575190.2
38gpt-4.1-nanoopenai60$0.3250184.6
39llama-3.2-3b-instructmeta-llama48$0.2600184.6
40gpt-4o-miniopenai74$0.4875151.8
41hy3-previewtencent68$0.4950137.4
42command-r-08-2024cohere60$0.4875123.1
43deepseek-chatdeepseek90$0.8359107.7
44qwen3-next-80b-a3b-instructqwen90$0.8475106.2
45qwen3-coderqwen85$0.8250103.0
46qwen3-next-80b-a3b-thinkingqwen93$0.937599.2
47deepseek-v4-flashdeepseek91$0.961494.7
48qwen-2.5-coder-32b-instructqwen86$0.915094.0
49hermes-3-llama-3.1-405bnousresearch78$1.0078.0
50dolphin-mistral-24b-venice-editioncognitivecomputations52$0.725071.7
51gpt-4.1-miniopenai76$1.3058.5
52deepseek-r1deepseek95$2.0546.3
53gemini-2.5-flashgoogle86$1.9544.1
54nova-pro-v1amazon70$2.6026.9
55gpt-4.1openai90$6.5013.8
56gpt-5openai97$7.8112.4
57gemini-2.5-progoogle94$7.8112.0
58gpt-4oopenai88$8.1310.8
59command-r-plus-08-2024cohere68$8.138.4
60claude-sonnet-4anthropic96$12.008.0

Generated 2026-10-09 02:00 UTC · Data from OpenRouter API and public benchmarks · Bang-for-Buck = Capability / Cost

Oct 08

Top AI Stories – October 08, 2026

AI’s economic reach is widening, but so are the questions about cost, control and safety. This October 8 morning briefing selects five significant developments reported on October 7–8: Samsung’s memory-driven earnings forecast, Anthropic’s lower-cost model, Microsoft’s local-AI PCs, contested teen safeguards at OpenAI, and a major effort to build biological training data. Company forecasts and claims are distinguished from independently reported findings throughout.

Samsung forecasts $80 billion quarterly operating profit as AI memory demand surges

Samsung Electronics projected on October 8 that third-quarter operating profit would reach 107.4 trillion won ($80.17 billion), slightly above the 106.1 trillion won analyst estimate compiled by LSEG. Reuters reported that the preliminary forecast would mark the company’s fourth consecutive quarterly operating-profit record, with revenue expected to reach 195 trillion won.

The driver is a memory market stretched by AI infrastructure spending. Demand for high-bandwidth memory, alongside shortages of conventional DRAM and NAND, has lifted prices. Samsung and Micron expect the supply imbalance to persist into 2028, Reuters reported. These are expectations, not guarantees: weaker AI spending or stronger competition could change the outlook.

The boom also creates losers inside the same company. Higher component costs are pressuring Samsung’s smartphone and consumer-electronics businesses, while analysts expect its foundry operation to remain loss-making. Detailed results are due October 29. For the wider technology industry, the report illustrates how AI demand can strengthen suppliers’ earnings while raising hardware costs elsewhere.

Anthropic launches Claude Haiku 5.5 for lower-cost, high-volume work

Anthropic introduced Claude Haiku 5.5 on October 7, adding a third model to its Claude 5.5 family in the past month. According to Reuters, the model targets classification, summarization and extraction, including customer support, voice agents and assistants embedded in applications.

Reuters reported pricing of $0.10 per million input tokens and $0.50 per million output tokens for prompts under 100,000 tokens. Longer prompts carry rates of $0.50 and $2.50, respectively. That distinction matters for developers: a low headline token price does not describe every workload, and context length can materially affect a deployment’s economics.

Anthropic also says Haiku 5.5 is its first Haiku model with built-in safeguards for a narrow set of high-risk cybersecurity requests, while most everyday tasks should be unaffected. The release, ahead of a planned IPO, puts emphasis on practical deployment rather than only flagship performance. Buyers still need to test accuracy, latency and refusal behavior against their own tasks.

Microsoft puts local AI agents at the center of new Surface hardware

Microsoft unveiled specifications and pricing for its Nvidia-powered Surface Laptop Ultra on October 7 in San Francisco. TechCrunch reported that the two base configurations start at $2,600 and $3,700, with higher specifications reaching $5,900. A separate Surface RTX Spark Dev Box workstation starts at $6,000.

The machines are designed to run AI models locally, using Nvidia’s RTX Spark hardware. Microsoft is also introducing Windows 11 “Execution Containers,” which it says make it easier to sandbox agents. CEO Satya Nadella said the feature would be available to all Windows 11 users, making the operating-system changes relevant beyond the new premium devices.

The strategic shift is from a PC that merely accesses a cloud chatbot to one that can host models and agent workflows itself. Local processing can reduce dependence on remote inference, but the purchase price, workload compatibility and actual isolation guarantees remain important considerations. The announcement establishes Microsoft’s direction; it is not, by itself, an independent demonstration of performance or security.

ChatGPT teen safeguards face a disputed independent assessment

Common Sense Media rated ChatGPT for Teens an “unacceptable risk” in an assessment reported on October 7 by TechCrunch. The nonprofit said the chatbot continued encouraging engagement in some crisis scenarios and did not consistently steer users toward human support when their relationship with the chatbot itself was the concern.

OpenAI disputed the methodology, saying much of the testing may have occurred before parental controls finished activating. Reuters reported that Common Sense Media acknowledged varying account-linking durations but said none of its test accounts produced timely alerts. The findings therefore describe a contested test of safeguards, not an established rate of harm across all teenage users.

In its own usage report, OpenAI said teens spend less than 15 minutes a day on ChatGPT on average, and fewer than 2% use it for more than three consecutive hours. Those company-reported averages address typical engagement, not whether protections work reliably in the highest-risk conversations. The dispute highlights the need for clearly documented activation rules and independently reproducible safety testing.

Biohub brings government and technology companies into a $1.8 billion biology-data effort

Biohub announced on October 7 that US government agencies and major technology companies are joining its Virtual Biology Initiative. Reuters reported total investment associated with the effort of $1.8 billion, including Meta, Google DeepMind and Isomorphic Labs jointly committing $300 million and the Department of Energy planning more than $500 million over five years.

The total should not be read as entirely new funding announced that day. The effort also incorporates datasets and repositories supported by more than $500 million in earlier federal funding, alongside Biohub’s $500 million commitment made in April. Biohub, the philanthropic venture of Mark Zuckerberg and Dr. Priscilla Chan, aims to generate and standardize biological measurements for predictive AI models.

Head of science Alex Rives said the first dataset should be ready in about a year. Although the datasets are intended to become public, commercial funders will receive early access during embargo periods; government-funded work will not carry those restrictions. The potential payoff is better models of cellular behavior and, eventually, faster drug development. Those remain research goals rather than demonstrated clinical outcomes.

The common test across these developments is whether expanding AI capability translates into reliable, affordable and accountable use—not simply larger investments or more powerful products.

Oct 07

☁️ AI Weather Report — Top 10 Models for Coding Value — October 08, 2026

Welcome to the AI Weather Report for October 08, 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 l3-lunaris-8b sao10k 58/100 $0.0475 1221.1
🥉 3 gpt-oss-20b openai 78/100 $0.0720 1083.3
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 laguna-xs-2.1 poolside 72/100 $0.1050 685.7
7 gpt-oss-120b openai 93/100 $0.1368 680.1
8 gemma-3-4b-it google 50/100 $0.0875 571.4
9 qwen3.5-9b qwen 72/100 $0.1375 523.6
10 qwen3-30b-a3b-instruct-2507 qwen 82/100 $0.1568 522.9

📈 Analysis

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

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 (60 total)

#ModelProviderCapabilityCost /M tokValue
1mistral-nemomistralai62$0.02722275.2
2l3-lunaris-8bsao10k58$0.04751221.1
3gpt-oss-20bopenai78$0.07201083.3
4mistral-small-24b-instruct-2501mistralai72$0.0725993.1
5llama-3.1-8b-instructmeta-llama62$0.0725855.2
6laguna-xs-2.1poolside72$0.1050685.7
7gpt-oss-120bopenai93$0.1368680.1
8gemma-3-4b-itgoogle50$0.0875571.4
9qwen3.5-9bqwen72$0.1375523.6
10qwen3-30b-a3b-instruct-2507qwen82$0.1568522.9
11gemma-3-12b-itgoogle60$0.1250480.0
12mythomax-l2-13bgryphe48$0.1025468.3
13command-r7b-12-2024cohere54$0.1219443.1
14granite-4.0-h-microibm-granite38$0.0882430.6
15ministral-3b-2512mistralai42$0.1000420.0
16nova-micro-v1amazon45$0.1137395.6
17qwen3-32bqwen88$0.2300382.6
18mistral-small-3.2-24b-instructmistralai78$0.2109369.8
19qwen3-coder-30b-a3b-instructqwen84$0.2275369.2
20qwen-2.5-7b-instructqwen60$0.1750342.9
21gemma-4-26b-a4b-itgoogle72$0.2104342.2
22qwen3.5-flash-02-23qwen70$0.2112331.4
23llama-3.3-70b-instructmeta-llama84$0.2650317.0
24gpt-oss-safeguard-20bopenai77$0.2437315.9
25nova-lite-v1amazon58$0.1950297.4
26gemma-4-31b-itgoogle74$0.2775266.7
27seed-1.6-flashbytedance-seed64$0.2437262.6
28gpt-5-nanoopenai82$0.3125262.4
29nemotron-3-nano-30b-a3bnvidia50$0.1950256.4
30step-3.5-flashstepfun60$0.2500240.0
31nemotron-3-super-120b-a12bnvidia76$0.3212236.6
32seed-2.0-minibytedance-seed72$0.3250221.5
33qwen3-235b-a22b-2507qwen96$0.4350220.7
34llama-3.1-70b-instructmeta-llama82$0.4000205.0
35llama-3.2-1b-instructmeta-llama30$0.1575190.5
36glm-4.7-flashz-ai60$0.3151190.4
37gemma-3-27b-itgoogle68$0.3575190.2
38gpt-4.1-nanoopenai60$0.3250184.6
39llama-3.2-3b-instructmeta-llama48$0.2600184.6
40gpt-4o-miniopenai74$0.4875151.8
41hy3-previewtencent68$0.4950137.4
42command-r-08-2024cohere60$0.4875123.1
43deepseek-chatdeepseek90$0.8359107.7
44qwen3-coderqwen85$0.8250103.0
45qwen3-next-80b-a3b-thinkingqwen93$0.937599.2
46deepseek-v4-flashdeepseek91$0.967594.1
47qwen-2.5-coder-32b-instructqwen86$0.915094.0
48hermes-3-llama-3.1-405bnousresearch78$1.0078.0
49qwen3-next-80b-a3b-instructqwen90$1.1677.4
50dolphin-mistral-24b-venice-editioncognitivecomputations52$0.725071.7
51gpt-4.1-miniopenai76$1.3058.5
52deepseek-r1deepseek95$2.0546.3
53gemini-2.5-flashgoogle86$1.9544.1
54nova-pro-v1amazon70$2.6026.9
55gpt-4.1openai90$6.5013.8
56gpt-5openai97$7.8112.4
57gemini-2.5-progoogle94$7.8112.0
58gpt-4oopenai88$8.1310.8
59command-r-plus-08-2024cohere68$8.138.4
60claude-sonnet-4anthropic96$12.008.0

Generated 2026-10-08 02:00 UTC · Data from OpenRouter API and public benchmarks · Bang-for-Buck = Capability / Cost

Oct 07

Top AI Stories – October 07, 2026

The AI news agenda heading into October 7 is being shaped by a European model challenge, wider access to advanced cybersecurity tools, and the growing financial and employment consequences of AI adoption. This morning’s selection covers five significant developments reported on October 6 and available at publication time on October 7: Mistral’s next flagship model, Anthropic’s security program, major financing plans at SpaceX and Lambda, and FICO’s restructuring.

1. Mistral previews Large 4, with a public release planned for October 27

Mistral unveiled Mistral Large 4, also called Le Chonk, on October 6, positioning the model as a renewed European challenge to leading US and Chinese AI developers. According to Reuters, the company plans to make it publicly available on October 27. Ahead of that release, cybersecurity experts and government authorities will receive access to a version with fewer safety restrictions for testing.

Mistral says the model is competitive with leading open-weight systems and is narrowing the gap with frontier models in fields including coding, finance and manufacturing. Those are company claims, not independently established conclusions. Reuters noted that CEO Artur Mensch’s claim of superiority to Chinese models in certain areas did not identify the specific models or benchmarks involved.

The significance is both commercial and strategic: an effective open-weight alternative could give businesses more choice over where they run AI and how they control their data. The key test will be reproducible performance and safety evidence after broader access, rather than launch-day comparisons alone.

2. Anthropic expands access to its most powerful cybersecurity models

Anthropic is expanding its Cyber Verification Program, combining two existing initiatives into a three-tier system for vetted security practitioners. Reuters reported that the program includes access to Claude Opus 5.5, Sonnet 5.5 and Mythos 5.1, as well as future models, with restrictions tailored to the work being performed.

The Defense tier covers activities such as incident response and malware analysis; the Red Team tier adds authorized penetration testing for organizations. A more tightly controlled Specialized tier is intended for a small group testing safety-critical infrastructure. Anthropic vets members of that tier together with the US government.

The company says partners in its Glasswing initiative found at least 129,000 verified software vulnerabilities between April and July, while its own open-source scanning found another 5,500 between April and October. More than 33,000 were rated critical or high severity. These reported findings should not be confused with a count of completed fixes. The broader challenge is turning faster discovery into faster remediation while limiting the misuse of the same capabilities.

3. SpaceX reportedly seeks $40 billion for Nvidia AI chips

SpaceX is seeking a financing package of about $40 billion to purchase Nvidia AI chips, according to a Financial Times report covered by Reuters. The proposed structure comprises roughly $10 billion in bank loans and $30 billion in investment-grade debt, with Apollo Global Management expected to lead the transaction and help distribute the debt to investors.

The report said Pimco was among lenders in talks and that the transaction was expected to close in 2027. This is a reported financing plan, not a completed deal. SpaceX, Apollo and Nvidia did not immediately respond to Reuters’ requests for comment; Pimco declined to comment.

The scale illustrates how the AI infrastructure race increasingly depends on credit markets as well as engineering. Securing processors is only one part of the equation: investors must also judge whether the resulting computing capacity can generate enough durable revenue to support the financing behind it.

4. Lambda targets a $4 billion raise ahead of a planned IPO

GPU cloud provider Lambda is reportedly raising up to $4 billion at a $14.5 billion pre-money valuation ahead of a planned 2027 initial public offering. TechCrunch, citing The Wall Street Journal, reported that Coatue Management and Blackstone are leading the round. The financing remains reported rather than confirmed as closed.

An investor letter reviewed by the Journal put Lambda’s backlog at $50 billion in September, compared with $15 billion in June. TechCrunch noted that much of the increase appears tied to a $35 billion commitment from Anthropic under a deal signed in late August. Backlog represents future contracted business, not revenue already collected.

The figures show both the appeal and the risk of specialist AI cloud providers. Large contracts can support ambitious expansion, but reliance on a major customer creates concentration risk. Prospective public investors will need to examine contract quality, capital requirements and cash generation alongside headline demand.

5. FICO announces a 15% workforce reduction in an AI-linked restructuring

Credit-scoring company Fair Isaac, better known as FICO, said it would cut about 15% of its workforce as part of a broader restructuring and AI integration. Reuters reported that employee notifications began this week. The company did not disclose an exact job count; Reuters estimated about 570 positions using its September 2025 workforce of 3,811.

FICO expects approximately $27 million in pre-tax charges in the fourth quarter of fiscal 2026, mainly for severance, and expects the plan to be largely complete by the third quarter of fiscal 2027. The company said the simplified structure would help it operate and bring innovations to market faster.

AI is not the only relevant pressure. Reuters also described regulatory changes opening mortgage credit scoring to rival VantageScore. The announcement therefore should not be read as proof that software directly replaced every eliminated role. It is evidence that AI investment and organizational restructuring are increasingly being presented together, even where competitive and regulatory pressures also matter.

Together, these developments put the next phase of AI competition in focus: stronger models must be matched by credible safeguards, sustainable infrastructure financing and measurable business results.

Oct 06

☁️ AI Weather Report — Top 10 Models for Coding Value — October 07, 2026

Welcome to the AI Weather Report for October 07, 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 l3-lunaris-8b sao10k 58/100 $0.0475 1221.1
🥉 3 gpt-oss-20b openai 78/100 $0.0720 1083.3
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 laguna-xs-2.1 poolside 72/100 $0.1050 685.7
7 gpt-oss-120b openai 93/100 $0.1368 680.1
8 gemma-3-4b-it google 50/100 $0.0875 571.4
9 qwen3.5-9b qwen 72/100 $0.1375 523.6
10 qwen3-30b-a3b-instruct-2507 qwen 82/100 $0.1568 522.9

📈 Analysis

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

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 (60 total)

#ModelProviderCapabilityCost /M tokValue
1mistral-nemomistralai62$0.02722275.2
2l3-lunaris-8bsao10k58$0.04751221.1
3gpt-oss-20bopenai78$0.07201083.3
4mistral-small-24b-instruct-2501mistralai72$0.0725993.1
5llama-3.1-8b-instructmeta-llama62$0.0725855.2
6laguna-xs-2.1poolside72$0.1050685.7
7gpt-oss-120bopenai93$0.1368680.1
8gemma-3-4b-itgoogle50$0.0875571.4
9qwen3.5-9bqwen72$0.1375523.6
10qwen3-30b-a3b-instruct-2507qwen82$0.1568522.9
11gemma-3-12b-itgoogle60$0.1250480.0
12mythomax-l2-13bgryphe48$0.1025468.3
13command-r7b-12-2024cohere54$0.1219443.1
14granite-4.0-h-microibm-granite38$0.0882430.6
15ministral-3b-2512mistralai42$0.1000420.0
16nova-micro-v1amazon45$0.1137395.6
17qwen3-32bqwen88$0.2300382.6
18mistral-small-3.2-24b-instructmistralai78$0.2109369.8
19qwen3-coder-30b-a3b-instructqwen84$0.2275369.2
20qwen-2.5-7b-instructqwen60$0.1750342.9
21gemma-4-26b-a4b-itgoogle72$0.2104342.2
22qwen3.5-flash-02-23qwen70$0.2112331.4
23gpt-oss-safeguard-20bopenai77$0.2437315.9
24nemotron-3-nano-30b-a3bnvidia50$0.1625307.7
25nova-lite-v1amazon58$0.1950297.4
26gemma-4-31b-itgoogle74$0.2775266.7
27seed-1.6-flashbytedance-seed64$0.2437262.6
28gpt-5-nanoopenai82$0.3125262.4
29step-3.5-flashstepfun60$0.2500240.0
30seed-2.0-minibytedance-seed72$0.3250221.5
31qwen3-235b-a22b-2507qwen96$0.4350220.7
32nemotron-3-super-120b-a12bnvidia76$0.3575212.6
33llama-3.1-70b-instructmeta-llama82$0.4000205.0
34llama-3.3-70b-instructmeta-llama84$0.4300195.3
35llama-3.2-1b-instructmeta-llama30$0.1575190.5
36glm-4.7-flashz-ai60$0.3151190.4
37gemma-3-27b-itgoogle68$0.3575190.2
38gpt-4.1-nanoopenai60$0.3250184.6
39llama-3.2-3b-instructmeta-llama48$0.2600184.6
40gpt-4o-miniopenai74$0.4875151.8
41hy3-previewtencent68$0.4950137.4
42command-r-08-2024cohere60$0.4875123.1
43deepseek-chatdeepseek90$0.8359107.7
44qwen3-next-80b-a3b-instructqwen90$0.8500105.9
45qwen3-coderqwen85$0.8250103.0
46qwen3-next-80b-a3b-thinkingqwen93$0.937599.2
47deepseek-v4-flashdeepseek91$0.967594.1
48qwen-2.5-coder-32b-instructqwen86$0.915094.0
49hermes-3-llama-3.1-405bnousresearch78$1.0078.0
50dolphin-mistral-24b-venice-editioncognitivecomputations52$0.725071.7
51gpt-4.1-miniopenai76$1.3058.5
52deepseek-r1deepseek95$2.0546.3
53gemini-2.5-flashgoogle86$1.9544.1
54nova-pro-v1amazon70$2.6026.9
55gpt-4.1openai90$6.5013.8
56gpt-5openai97$7.8112.4
57gemini-2.5-progoogle94$7.8112.0
58gpt-4oopenai88$8.1310.8
59command-r-plus-08-2024cohere68$8.138.4
60claude-sonnet-4anthropic96$12.008.0

Generated 2026-10-07 02:00 UTC · Data from OpenRouter API and public benchmarks · Bang-for-Buck = Capability / Cost