Kimi K2.7 Code vs GPT-5.4 vs GPT-5.5: Token Costs, Benchmarks, and Architecture Compared

If you are building an AI-powered coding tool or just trying to decide which API to plug into your workflow, the question is no longer just “which model writes better code?” It is “which model writes better code for the price?”

With Moonshot AI’s Kimi K2.7 Code and OpenAI’s GPT-5.4 and GPT-5.5 all on the table, the gap in reasoning quality has narrowed significantly. But the gap in pricing has not. Let’s break down the numbers, the benchmarks, and the architecture so you can make an informed decision.

Model Architecture and Size

Before we look at performance, it helps to understand what is running under the hood.

Kimi K2.7 Code uses a Mixture-of-Experts architecture with 1 trillion total parameters but only activates 32 billion per token. This is the key to its efficiency: you get the knowledge capacity of a massive model with the inference cost of a much smaller one. OpenAI does not disclose parameter counts or architecture details for GPT-5.4 or GPT-5.5.

Token Pricing: The Hard Numbers

All prices are per 1 million tokens, standard tier.

The first thing that jumps out: GPT-5.5 output tokens cost 7.5x more than Kimi K2.7 Code. Even GPT-5.4 output is 3.75x more expensive. For a coding session where the model generates hundreds of lines of code, this difference compounds fast.

Kimi also offers a HighSpeed variant that pushes output to roughly 180 tokens per second (up to 260 tok/s in short contexts). Even at double the standard price, it is still roughly half the cost of GPT-5.4 output ($8.00 vs $15.00).

GPT-5.4-mini is price-competitive with Kimi K2.7 Code on output, but it is a significantly smaller model with less reasoning depth, as the benchmarks will show.

Coding Benchmarks

Here is where the rubber meets the road. These are the published benchmark scores from each model’s official release pages.

Kimi K2.7 Code vs GPT-5.5 (with Claude Opus 4.8 for reference)

Kimi K2.7 Code shows substantial gains over K2.6: +21.8% on Kimi Code Bench v2, +11.0% on Program Bench, and +31.5% on MLS Bench Lite. It is competitive with GPT-5.5 on MLS Bench Lite (35.1 vs 35.5) but trails on the other two coding benchmarks.

Agentic Benchmarks

On agentic tasks, K2.7 Code improves roughly 10% over K2.6 across the board. GPT-5.5 leads on MCP Mark Verified (92.9) and Kimi Claw 24/7 Bench (52.8), but K2.7 Code is within striking distance on MCP Atlas (76.0 vs 79.4).

OpenAI’s Published Benchmarks (GPT-5.4 and GPT-5.5)

GPT-5.5 represents a meaningful step up from GPT-5.4 on coding, particularly on Terminal-Bench 2.0 (+7.6 points). However, the price jump from $15/M to $30/M output tokens is significant.

Kimi K2.6 Published Benchmarks (for context)

Kimi K2.6, the predecessor to K2.7 Code, published scores against GPT-5.4:

Kimi K2.6 already matched or exceeded GPT-5.4 on several coding benchmarks. K2.7 Code improves further on these scores, though direct comparisons against GPT-5.4 on the same benchmarks are not yet published.

Note: the Terminal-Bench 2.0 scores differ between tables because Kimi evaluated GPT-5.4 using the Terminus-2 framework (65.4), while OpenAI’s own published score uses their internal evaluation harness (75.1). Different frameworks produce different results on the same benchmark.

Reasoning Capabilities

Thinking and Deep Reasoning

Kimi K2.7 Code always runs with thinking mode enabled. There is no non-thinking mode. The model internally chains multiple reasoning steps before producing code, and K2.7 Code reduces thinking-token usage by approximately 30% compared to K2.6. This means faster responses and lower effective cost.

GPT-5.4 and GPT-5.5 support configurable reasoning effort levels (from none to xhigh). You can trade off speed for depth depending on the task. GPT-5.5 is notably more token-efficient than GPT-5.4, often reaching higher-quality outputs with fewer tokens.

Context Window

Kimi K2.7 Code offers a 256K token context window as standard. This is critical for coding tasks where you need the model to understand an entire codebase, multiple files, or a long conversation history.

GPT-5.4 offers 128K tokens in its short context tier (at $2.50/$15.00). Extended context up to 1M tokens is available at $5.00/M input and $22.50/M output.

GPT-5.5 offers a 1M token context window as standard, priced at $5.00/M input and $30.00/M output.

Multimodal Input

All three models support image input. Kimi K2.7 Code also supports video input through its MoonViT vision encoder. GPT-5.4 and GPT-5.5 support native vision with configurable detail levels.

Tool Calling and Structured Output

Kimi K2.7 Code supports ToolCalls, JSON Mode, and Partial Mode natively. These are essential for building agentic coding workflows where the model needs to call APIs, return structured diffs, or stream partial results.

GPT-5.4 and GPT-5.5 support function calling, structured outputs, and tool search through the standard OpenAI API. GPT-5.5 introduced improved agentic tool calling with higher accuracy in fewer turns.

Real-World Cost Projection

Let’s say you run a coding agent that processes 50 requests per day. Each request averages 2,000 input tokens (the prompt and context) and 1,500 output tokens (the generated code). We will assume a 50% cache hit rate for input tokens.

Monthly cost (30 days):

Kimi K2.7 Code comes in at roughly 3.5x cheaper than GPT-5.4 and 7x cheaper than GPT-5.5 for the same workload. GPT-5.4-mini is price-competitive at $11.37/month, but it is a much smaller model with significantly less reasoning depth.

Now let’s scale this up to a production coding agent handling 1,000 requests per day:

At production scale, the cost difference between Kimi K2.7 Code and GPT-5.5 is over $1,300 per month. That is real money that could fund an entire additional service.

When to Use Which

Choose Kimi K2.7 Code when:

• You are building a coding agent or IDE plugin and need low latency at scale.
• You need a large context window (256K) to feed entire codebases.
• You want deep reasoning specifically tuned for code generation.
• Budget is a primary concern and you cannot afford GPT-5.4 or GPT-5.5 output costs.
• You want an open-source model you can self-host or fine-tune.

Choose GPT-5.4 when:

• You need broad general knowledge alongside coding (architecture decisions, system design).
• You are already deep in the OpenAI ecosystem and value API stability and tooling.
• You need the 1M token context window for very long sessions.
• Your tasks benefit from computer-use capabilities natively integrated into the model.

Choose GPT-5.5 when:

• You need the absolute best reasoning across all domains and budget is not a constraint.
• You are working on the hardest coding problems where a 7.6-point gain on Terminal-Bench 2.0 over GPT-5.4 justifies the cost.
• You need the 1M context window with state-of-the-art long-context performance.
• You are doing scientific research or complex multi-step agentic workflows.

Choose GPT-5.4-mini when:

• You want a budget-friendly option within the OpenAI ecosystem.
• Your coding tasks are relatively straightforward and do not require deep multi-step reasoning.
• You are prototyping and want to keep costs minimal.

The Bottom Line

Moonshot AI has positioned Kimi K2.7 Code as a direct competitor to GPT-5.4 and GPT-5.5 for coding workloads, and the pricing reflects an aggressive strategy. You get a model purpose-built for code, with a 256K context window, a mandatory thinking mode that is 30% more token-efficient than its predecessor, and an open-source Mixture-of-Experts architecture, all at a fraction of the cost.

GPT-5.5 is the stronger model on raw benchmarks. There is no debate there. But the question is whether a 7x price premium is worth the marginal improvement for your specific use case. For most coding-specific workloads in mid-2026, Kimi K2.7 Code hits the sweet spot of performance per dollar.

References

• Kimi K2.7 Code — Model Architecture and Benchmarks
• Kimi K2.7 Code API Pricing
• Introducing GPT-5.4 — OpenAI
• Introducing GPT-5.5 — OpenAI
• OpenAI API Pricing
• Kimi K2.6 Technical Blog