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*updated October 2026
In OpenAI’s GPT-6 lineup, the top model costs 100 times more per output token than the cheapest one. On DeepSWE v1.1, a long-horizon coding benchmark OpenAI reports for both, the gap between them is 7.5 points.
That’s the finding this AI model comparison for software development is built around: price and capability have spread so far apart that one model for everything is the most expensive habit a team can have.
If you’re the person who decides which models your developers get, or the one who opens the API invoice at the end of the month, this guide is for you.
The case for routing is simple: send each task to the model that fits it, and you ship faster and spend less than a team that defaults to the flagship for everything.
Below, I cover every major model family as it stands in October 2026: OpenAI, Anthropic, Google, DeepSeek, Qwen, Meta, Mistral, and a handful of others. You’ll get pricing, context windows, vendor-reported benchmarks, and my view on where each model earns its place.
How I put this together: prices, context windows, and benchmark scores come from official vendor docs and announcements. Prices and specs verified October 2026. All benchmarks are vendor-reported unless noted, and I explain how to read them below.
Key takeaways
Every major vendor sells models in tiers, and the tier matters more than the brand. Once you know which tier a task belongs to, the shortlist gets short fast.
Here’s how you can group them:
My recommendation is to start every evaluation in the workhorse tier. Move up only when a task fails there, and move down once you know a cheaper model handles it reliably.
The other split worth knowing is between fast responses and heavy reasoning.
Most models in this guide let you set a reasoning effort level, so one model can behave like a quick assistant or a slow, careful planner. I cover what that does to your bill further down.
Treat every coding benchmark in this article as directional. The numbers are real, but vendors run them on their own setups, and the setup can move a score by several points.
OpenAI stopped reporting SWE-bench Verified in February 2026, citing flawed tests and contamination (models seeing test data during training).
Most other vendors headline newer benchmarks instead.
You’ll still see it in some places. Mistral and Qwen both quote it for some models. When you do, I’d discount it heavily.
These two are the closest thing to cross-vendor columns in 2026:
The catch is that each vendor runs them with its own agent harness (the scaffolding, tools, and time limits around the model). Different harness, different score.
Version numbers matter too. Google reports Gemini 3.8 Flash at 89.4% on Terminal-Bench 2.1 and 19.1% on Terminal-Bench 4.0. Same model, very different test.
Anthropic reports Sonnet 5.5 at 70.6% on Terminal-Bench 4.0 and Opus 5.5 at 66.4%. The cheaper model wins.
Opus 5.5 leads on Anthropic’s other coding results, CursorBench 4.0 (57.8% vs. 55.5%) and FrontierCode v1.1.
My read is that Terminal-Bench rewards fast, iterative command-line work, where a quicker model fits more attempts into the same time budget.
Anthropic doesn’t officially explain the gap, so take that as my interpretation.
The practical lesson: pick the benchmark that looks most like your work, and then test on your own codebase anyway.
This table covers the models I’d consider for development work in October 2026. Prices are USD per million tokens. Benchmarks are vendor-reported unless noted.
| Model | Input | Output | Context | Headline coding benchmark | Best for |
|---|---|---|---|---|---|
| GPT-6 Astra | $10 (1) | $50 (1) | 1.05M | DeepSWE v1.1 74.1%, Terminal-Bench 4.0 57.9% | Hardest agentic tasks where cost is secondary |
| GPT-6.1 Sol | $2 (1) | $10 (1) | 1.05M | DeepSWE v1.1: OpenAI reports it matches GPT-6 Astra | Daily coding, multi-file changes |
| GPT-6 Luna | $0.10 | $0.50 | 1.05M | DeepSWE v1.1 66.6% (max effort) | High-volume work, subagents |
| Claude Fable 5.1 | $10 | $50 | 1M | Terminal-Bench 4.0 55.8% | Long unattended runs, complex prototypes |
| Claude Opus 5.5 | $4 | $20 | 1M | Terminal-Bench 4.0 66.4%, CursorBench 4.0 57.8% | Complex refactoring, the default for hard work |
| Claude Sonnet 5.5 | $2 | $10 | 1M | Terminal-Bench 4.0 70.6% | Daily driver, terminal-based agents |
| Claude Haiku 5.5 | $0.10 (2) | $0.50 (2) | 1M | Terminal-Bench 4.0 39.2% | Subagents, high-volume agent work |
| Gemini 4 Argon (3) | $2 intro, $4 standard | $10 intro, $20 standard | Not stated (1M max output) | DeepSWE v1.1 77.9%, Terminal-Bench 4.0 57.4% | Limited access, long-horizon work |
| Gemini 3.8 Flash | $0.75 (4) | $3.75 (4) | 1M | DeepSWE v1.1 73.7% | Fast, cheap iteration |
| Gemini 3.1 Pro (preview) | $2 (5) | $12 (5) | 1M | None new | Large-context reasoning |
| DeepSeek-V4.1-Flash | $0.30 (6) | $1.20 (6) | 1M | DeepSWE v1.1 74.2% (max effort) | Non-sensitive, cost-driven work |
| Qwen3.8-27B | Self-hosted | Self-hosted | 262K (up to 1M) | SWE-bench Pro 61.7% | Local coding on modest hardware |
| Qwen3.8-Flash-Next | Self-hosted | Self-hosted | 262K (up to 1M) | SWE-bench Pro 62.5% | Local coding, check the license |
| Qwen3-Coder-Next | Self-hosted | Self-hosted | 262K | SWE-bench Pro 44.3% | Small local coding agents |
| Muse Spark 1.3 | $1.25 (7) | $4.25 (7) | 1M | None verified | Low-cost API work |
| Muse Glimmer | Self-hosted | Self-hosted | Not stated | None verified | Agents on a consumer GPU |
| Mistral Medium 3.5 | $1.50 | $7.50 | 256K | SWE-bench Verified 77.6% (benchmark OpenAI dropped) | EU-provider general and coding work |
| Mistral Large 4 (preview) | $0.68 (8) | $2.09 (8) | 1M | DeepSWE v1.1 61.7%, Terminal-Bench 4.0 28.3% | EU-trained, EU-hosted frontier work |
| Codestral 25.08 | $0.30 | $0.90 | 128K | None new | Code completion, fill-in-the-middle |
| Mistral Large 3 | $0.50 | $1.50 | 256K | None new | Multilingual, general-purpose work |
Table notes:
Each model’s section below links to the vendor page behind these numbers, so you can check a price before you commit to it.
OpenAI’s GPT-6 lineup covers all three paid tiers, and every model in it shares the same 1.05M-token context window and 128K max output. That makes switching between them inside one workflow painless.
GPT-6 Astra is OpenAI’s highest-capability model, released in September 2026. It costs $10 per million input tokens, $1 for cached input, and $50 per million output tokens.
OpenAI reports 74.1% on DeepSWE v1.1 and 57.9% on Terminal-Bench 4.0. The API docs list a 1.05M context window, with the long-prompt surcharge above 272K input tokens.
I’d reserve Astra for long agentic runs where a failed attempt costs you more than the tokens do. For most tickets, it’s overkill.
Use GPT-6 Astra for: long-horizon agent tasks, complex debugging across services, and work where accuracy matters far more than cost.
GPT-6.1 Sol is the OpenAI model I’d point most teams to first. It costs $2 input, $0.10 cached, and $10 output per million tokens.
OpenAI says it matches GPT-6 Astra on DeepSWE v1.1, though it hasn’t published an absolute score. If that holds on your codebase, you’re getting frontier-level coding at a fifth of the price.
Use GPT-6.1 Sol for: feature work, multi-file changes, code review, and repo-wide reasoning.
GPT-6 Luna costs $0.10 input and $0.50 output per million tokens, with cached input at $0.01. OpenAI reports 66.6% on DeepSWE v1.1 at maximum reasoning effort.
That score is the reason Luna belongs in your stack. It’s cheap enough to run as a subagent on every file in a repo, and capable enough that the output is worth reviewing.
Use GPT-6 Luna for: subagents, test generation, documentation, batch refactors, and IDE assistance.
Anthropic’s lineup is the one I’d look at first for agentic coding, mostly because of how well Opus 5.5 and Sonnet 5.5 hold up over long, multi-step tasks.
All four current models share a 1M-token context window and 128K max output. Anthropic’s pricing page shows no long-context premium for Fable 5.1, Opus 5.5, or Sonnet 5.5. Haiku 5.5 is the exception, with a higher rate above 100K tokens.
Claude Fable 5.1 costs $10 input and $50 output per million tokens, with cache hits at $0.25. Anthropic describes it as one of its most advanced models for coding and knowledge work.
Anthropic reports 55.8% on Terminal-Bench 4.0 and 73.4% on CursorBench 3.2.0. Note that’s an older CursorBench version than the one Anthropic uses for Opus 5.5, so the two scores don’t compare directly.
Fable makes sense for runs that last hours without a human checking in. For anything shorter, Opus 5.5 gives you most of the capability for 40% of the price.
Claude Mythos 5.1 is technically identical to Fable 5.1 with different safeguards, and Anthropic restricts it to vetted cyberdefenders and life scientists.
Use Claude Fable 5.1 for: long unattended agent runs that last hours, complex prototypes, and high-stakes work where a failed run costs more than the premium.
Claude Opus 5.5 costs $4 input and $20 output per million tokens, with cache hits at $0.20. Fast mode costs $8/$40.
Anthropic reports 66.4% on Terminal-Bench 4.0, 54.4% on FrontierCode v1.1, and 57.8% on CursorBench 4.0. Anthropic’s models overview recommends starting with Opus 5.5 for most workloads.
I agree with that for complex work. I wouldn’t use it for routine tickets, where Sonnet 5.5 does the job for half the price.
Use Claude Opus 5.5 for: major refactoring, new system design with full codebase context, complex multi-file changes, and long-running agent workflows.
Claude Sonnet 5.5 costs $2 input and $10 output per million tokens, with cache hits at $0.10. It posts the highest Terminal-Bench 4.0 score in this guide at 70.6%, plus 55.5% on CursorBench 4.0.
At this price, Sonnet 5.5 and GPT-6.1 Sol are the two models I’d test head-to-head before anything else. If you work in Claude Code, our Claude Code guide covers how to set it up.
Use Claude Sonnet 5.5 for: code generation, debugging, code review, terminal-based agent work, and everyday feature implementation.
Claude Haiku 5.5 is Anthropic’s fastest model, released in October 2026. It costs $0.10 input and $0.50 output per million tokens, the same as GPT-6 Luna, with a 1M-token context window. Prompts over 100K tokens cost five times as much ($0.50/$2.50).
Anthropic reports 39.2% on Terminal-Bench 4.0, against 16.4% for GPT-6 Luna in the same comparison. That makes it the strongest cheap model in this guide for command-line agent work, as long as you keep prompts under 100K.
Use Claude Haiku 5.5 for: subagents, high-volume agent tasks, test generation, code search and triage, and terminal work that doesn’t need a workhorse model.
Google’s lineup has the most interesting top model in this guide and the hardest one to get. Below Argon, Gemini 3.8 Flash is one of the strongest cheap models available.
Gemini 4 Argon is Google’s new frontier model, built for coding, knowledge work, and cybersecurity defense. Google announced it on 30 September 2026.
Access is limited. Google is rolling it out first to trusted cyber defenders through its Fairwind Program.
Google says it’ll make Argon available to developers, enterprises, and consumers “as soon as possible,” starting with paid API customers and Google AI Ultra subscribers. It isn’t listed on the Gemini API pricing page yet.
The numbers are strong. Google reports 77.9% on DeepSWE v1.1, the highest in this comparison.
Its Gemini model page adds 57.4% on Terminal-Bench 4.0 and 91.9% on Vibe Code Bench.
Pricing starts at an introductory $2 input and $10 output per million tokens, with cached input 95% off.
Standard pricing after that is $4/$20. Google hasn’t said how long the introductory period lasts.
Argon’s max output is 1M tokens, enough to generate very large changes in one run. Google hasn’t published an input context limit, so don’t assume one.
My advice: put Argon on your evaluation list, but don’t plan a roadmap around it until you have API access and a confirmed price.
Use Gemini 4 Argon for: long-horizon coding tasks and very large generated changes, once API access opens. Until then, keep it in evaluation and out of production.
Gemini 3.8 Flash is generally available and costs $0.75 input and $3.75 output per million tokens until 31 December 2026. After that, the Gemini API pricing page lists $1.50/$7.50.
It supports a 1,048,576-token input window and 65,536 output tokens. Google reports 73.7% on DeepSWE v1.1, which is close to GPT-6 Astra at a fraction of the price.
The weak spot is Terminal-Bench 4.0, where Google reports 19.1%. I’d keep it away from command-line agent work.
Use Gemini 3.8 Flash for: rapid iteration, batch processing, IDE assistance, and cost-sensitive work over large context.
Gemini 3.1 Pro is in preview. It costs $2/$12 per million tokens for prompts up to 200K and $4/$18 above that, with a 1M input window and 65,536 output tokens.
With Gemini 3.8 Flash scoring so well, I’d only reach for 3.1 Pro if you’ve tested it on a specific long-context task and it wins.
Use Gemini 3.1 Pro for: large-context reasoning across big codebases or long documentation, where your own tests show it beating Gemini 3.8 Flash.
DeepSeek offers some of the best price-to-performance in this guide, with real data risks attached. Both sides deserve equal weight.
DeepSeek-V4.1-Flash is DeepSeek’s main API model, released in September 2026 under the MIT license. Its API ID is deepseek-flash.
DeepSeek’s pricing page lists peak rates of $0.30 input and $1.20 output per million tokens, and off-peak rates of $0.15/$0.60. Cache hits cost $0.006 at peak and $0.003 off-peak. Peak hours run 01:00 to 04:00 and 06:00 to 10:00 UTC on weekdays.
It supports a 1M context window and 384K max output. DeepSeek reports 74.2% on DeepSWE v1.1 at max effort and 31.2% on Terminal-Bench 4.0.
DeepSeek’s pricing page also lists DeepSeek-V4-Pro at $1.32/$3.96 peak.
DeepSeek’s September announcement says that from 14 September 2026, deepseek-v4-pro requests route to V4.1-Flash at V4.1-Flash rates until V4.1-Pro launches. If you call the Pro ID, you’re getting Flash.
Use DeepSeek-V4.1-Flash for: cost-sensitive coding on non-sensitive code, batch jobs, and experimentation.
DeepSeek’s privacy policy states that it collects account data, prompts, uploaded files, chat history, and device and network data, and that it stores this information on servers in the People’s Republic of China.
The track record adds to the concern:
That last one includes a useful detail. NÚKIB’s warning excludes open-source DeepSeek models deployed locally with no connection to DeepSeek’s servers.
That’s where I’d draw the line too.
I wouldn’t send client code or regulated data to DeepSeek’s hosted API. Running the MIT-licensed weights on your own infrastructure is a different risk profile, and a reasonable one to consider.
Qwen gives you capable open-weight coding models at sizes you can run on your own hardware. If keeping code inside your network is a requirement, start here.
Qwen3.8-27B is released under the Apache 2.0 license. It supports a 262K context window, extendable to 1M.
Qwen reports 61.7% on SWE-bench Pro, 73.0% on Terminal-Bench 2.1, and 42.2% on DeepSWE v1.1. For a 27B model, that’s a strong result.
Use Qwen3.8-27B for: privacy-critical work, on-premise coding agents, and teams with modest GPU budgets.
Qwen3.8-Flash-Next is a mixture-of-experts model (a design that activates only part of the model per token) with 125B total and 6B active parameters. Qwen reports 62.5% on SWE-bench Pro and 58.7% on DeepSWE v1.1.
The catch is the license. It ships under the Qwen Community 1.0 license instead of Apache 2.0, so get your legal team to read it before you deploy it commercially.
Use Qwen3.8-Flash-Next for: on-premise coding agents that need more capability than Qwen3.8-27B, once your legal team has cleared the license.
Qwen3-Coder-Next is an 80B-parameter model with 3B active parameters, under Apache 2.0, with a 262K context window. Qwen reports 44.3% on SWE-bench Pro and 36.2% on Terminal-Bench 2.0.
It’s built for coding only, and the low active parameter count makes it fast on local hardware. For a deeper look at running models yourself, see our guide to local LLMs.
Use Qwen3-Coder-Next for: fast local coding agents, code completion on modest hardware, and coding-only workloads that must stay inside your network.
Meta’s lineup splits into a cheap API model and a small open-weight model. Both are worth a look, but read the data terms first.
Muse Spark 1.3 is a closed model, available through Meta’s Model API and Muse Code.
It costs $1.25 input and $4.25 output per million tokens, with cached input at $0.15, and Meta’s model docs list a 1,048,576-token context window.
Meta also offers a contributor tier at $0.10/$0.20, listed on its pricing page. The trade-off is that Meta can train on your prompts and outputs.
For client or proprietary code, I’d treat the contributor tier as off-limits. For open-source work or internal experiments, it’s one of the cheapest options going.
Meta’s announcement shows its coding results only in a chart, so test Muse Spark 1.3 yourself before you route serious work to it.
Use Muse Spark 1.3 for: low-cost API work on non-sensitive code, open-source projects, and internal experiments. Use the contributor tier only where Meta training on your data is acceptable.
Muse Glimmer is a 30B open-weight agentic model under Apache 2.0. Meta says a quantized version fits on a consumer GPU with 24 or 32GB of memory.
Meta’s announcement doesn’t list coding scores in its text. Its appeal is the license and the hardware footprint: a capable local agent that fits on a single workstation card.
Llama 4 Scout and Maverick are still downloadable under the Llama 4 Community License, which carries usage restrictions. For new local deployments, I’d look at Muse Glimmer or Qwen3.8 first.
Use Muse Glimmer for: local agents on a single workstation GPU, privacy-first prototyping, and teams that want Apache 2.0 open weights.
Mistral is the pick when keeping data with an EU provider matters. It’s a European company, and its privacy policy says it prioritizes EU-based providers. When personal data does leave the EU, it uses GDPR Article 46 safeguards.
Mistral Large 4 goes a step further: Mistral trained it in its own European datacenters and serves the preview on the same infrastructure.
Mistral Large 4 is Mistral’s largest model, in public preview since October 2026. It’s a mixture-of-experts model with 1.05T total and 52B active parameters and a 1M-token context window.
Preview pricing is $0.68 input and $2.09 output per million tokens, half the $1.36/$4.18 list price, with no announced end date.
Mistral reports 61.7% on DeepSWE v1.1 and 28.3% on Terminal-Bench 4.0, behind the US workhorse models on agentic coding.
Its edge is location: Mistral trained it and serves it from its own European datacenters.
Use Mistral Large 4 for: frontier-level work where EU training and hosting matter, multilingual tasks.
Mistral Medium 3.5 is a 128B dense model with open weights under a modified MIT license. Through Mistral’s API, it costs $1.50 input and $7.50 output per million tokens, with a 256K context window.
Mistral reports 77.6% on SWE-bench Verified. Given the benchmark’s known problems, I’d read that as “competitive” and test it on your own work.
Use Mistral Medium 3.5 for: general and coding work where an EU provider matters, and teams that want the option to self-host the same model later.
Codestral 25.08 is Mistral’s coding specialist for code completion, fill-in-the-middle (completing code between existing lines), and generation. It costs $0.30/$0.90 with a 128K context window. Low latency makes it a good fit for IDE autocomplete.
Mistral Large 3 is an open-weight general-purpose multimodal model at $0.50/$1.50 with a 256K window. I’d use it for multilingual work and general tasks where Medium 3.5 is more than you need.
Use Codestral 25.08 for: IDE autocomplete, fill-in-the-middle completion, and low-latency code generation with an EU provider.
Use Mistral Large 3 for: multilingual work and general-purpose tasks where Medium 3.5 is more than you need.
A few more models show up in IDE pickers and procurement conversations. None of them would be my first pick for coding, but each fills a niche.
I’d keep Gemma 4 on the shortlist for local deployments alongside Qwen3.8-27B. The rest are worth a test if price or hosting is your main constraint.
The list price per token tells you less than you’d think about what a task will cost. Two things move the bill more: reasoning effort and pricing rules.
Reasoning effort is a setting that controls how long a model “thinks” before answering. Most vendors offer levels such as low, medium, high, and max.
Higher effort usually improves results on hard problems, but the model generates more output tokens, and you pay for every one.
GPT-6 Luna’s 66.6% and DeepSeek-V4.1-Flash’s 74.2% on DeepSWE v1.1 are both max-effort scores.
My rule: start at medium effort and raise it only for tasks that fail. A cheap model at max effort can cost more than a mid-tier model at low effort.
Before you compare models on price, check these:
Model your costs against a real week of usage, including retries. That’s the number your finance team will see.
The simplest method works best: test models on your own codebase, inside your own tools, and measure quality, latency, and cost together. Benchmarks narrow the list. Your tickets make the decision.
A practical AI coding stack has three slots:
Two vendors in the first two slots is enough. It gives you a fallback when one has an outage or a price change, without doubling your evaluation work.
Closed models give you convenience and, for now, the highest scores on the hardest tasks. Open-weight models give you control over where data goes and how costs behave at scale.
The trade-off is operational. Self-hosting means you own infrastructure, updates, and monitoring. I’d only take that on when data control is a requirement, or when your volume makes API pricing hurt.
At DECODE, we work with AI agents every day, and senior engineers review everything they produce. The model choice matters, and review is what keeps quality steady when you switch models.
A stronger model makes fewer mistakes. It doesn’t make zero. Judge a model by how easy its mistakes are to catch, as much as by how often it makes them.
Choosing an AI model today isn’t about picking the “best” one. It’s about picking the right one for your product, your team, and your constraints.
Each model we’ve covered has a clear role. Some are great for deep reasoning. Others shine in speed, cost, or flexibility. The real value comes from knowing when to use each of them.
In practice, most dev teams don’t rely on a single model.
The key is to stay pragmatic. Test in your own environment, measure what actually matters to your product, and avoid overengineering your setup too early.
Get this right, and everything else gets easier.
Skilled in React Native, iOS and backend, Toni has a demonstrated knowledge of the information technology and services industry, with plenty of hands-on experience to back it up. He’s also an experienced Cloud engineer in Amazon Web Services (AWS), passionate about leveraging cloud technologies to improve the agility and efficiency of businesses. One of Toni’s most special traits is his talent for online shopping. In fact, our delivery guy is convinced that ‘Toni Vujević’ is a pseudonym for all DECODErs.
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